## 4 Computational Experiments
### Comparison with the State-of-the-Art Algorithms
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Problem Scales** & **Indicators** & **DRLLA** & **TRFM** & **PtrNet** & **RLPT** & **HDABC** & **RLGA** & **ISA** & **IACO** \\ \hline \multirow{4}{*}{50-task} & MPR & **1.0** & 0.922 & 0.713 & 0.406 & 0.841 & 0.793 & 0.848 & 0.661 \\  & MP & **271** & 250 & 187 & 110 & 229 & 215 & 232 & 179 \\  & MCN & **50** & 46 & 34 & 24 & 44 & 33 & 42 & 26 \\  & MCT (s) & 0.147 & 0.156 & 0.175 & **0.049** & 1706.386 & 32.484 & 11.932 & 4.492 \\  & MPR & **0.984** & 0.896 & 0.691 & 0.317 & 0.760 & 0.737 & 0.813 & 0.496 \\  & MP & **408** & 369 & 295 & 131 & 315 & 305 & 336 & 205 \\  & MCN & **73** & 68 & 53 & 29 & 51 & 45 & 61 & 30 \\  & MCT (s) & 0.148 & 0.244 & 0.246 & **0.079** & 1842.016 & 44.052 & 20.463 & 8.900 \\  & MPR & **0.953** & 0.851 & 0.621 & 0.245 & 0.701 & 0.627 & 0.749 & 0.419 \\  & MP & **522** & 466 & 340 & 134 & 384 & 343 & 412 & 230 \\  & MCN & **94** & 86 & 63 & 31 & 77 & 51 & 75 & 38 \\  & MCT (s) & 0.273 & 0.317 & 0.346 & **0.103** & 2033.880 & 55.394 & 22.037 & 15.256 \\  & MPR & **0.893** & 0.786 & 0.534 & 0.217 & 0.646 & 0.543 & 0.669 & 0.360 \\  & MP & **607** & 534 & 363 & 148 & 439 & 368 & 463 & 244 \\  & MCN & **110** & 99 & 68 & 34 & 71 & 54 & 83 & 45 \\  & MCT (s) & 0.309 & 0.387 & 0.463 & **0.144** & 2123.224 & 67.529 & 34.638 & 20.787 \\  & MPR & **0.822** & 0.710 & 0.474 & 0.185 & 0.603 & 0.486 & 0.554 & 0.316 \\  & MP & **677** & 585 & 390 & 152 & 496 & 400 & 458 & 260 \\  & MCN & **121** & 107 & 74 & 36 & 98 & 57 & 84 & 47 \\  & MCT (s) & 0.346 & 0.485 & 0.576 & **0.182** & 2571.790 & 77.264 & 45.204 & 32.148 \\  & MPR & **0.746** & 0.641 & 0.433 & 0.162 & 0.581 & 0.441 & 0.468 & 0.282 \\  & MP & **716** & 616 & 416 & 156 & 558 & 423 & 454 & 271 \\  & MCN & **127** & 113 & 80 & 37 & 97 & 60 & 81 & 48 \\  & MCT (s) & 0.481 & 0.558 & 0.641 & **0.222** & 2721.985 & 86.827 & 55.720 & 40.752 \\  & MPR & **0.687** & 0.588 & 0.400 & 0.146 & 0.500 & 0.399 & 0.417 & 0.255 \\  & MP & **754** & 644 & 439 & 160 & 548 & 438 & 460 & 279 \\  & MCN & **133** & 118 & 85 & 39 & 118 & 61 & 83 & 49 \\  & MCT (s) & 0.512 & 0.648 & 0.825 & **0.311** & 2818.767 & 95.474 & 67.816 & 51.379 \\  & MPR & **0.636** & 0.537 & 0.367 & 0.137 & 0.448 & 0.369 & 0.384 & 0.237 \\  & MP & **784** & 663 & 453 & 169 & 552 & 455 & 475 & 293 \\  & MCN & **138** & 121 & 89 & 40 & 96 & 62 & 86 & 50 \\  & MCT (s) & 0.574 & 0.718 & 1.014 & **0.382** & 2897.828 & 104.961 & 76.227 & 73.606 \\  & MPR & **0.586** & 0.501 & 0.350 & 0.119 & 0.384 & 0.336 & 0.324 & 0.221 \\  & MP & **805** & 689 & 481 & 163 & 527 & 461 & 446 & 304 \\  & MCN & **141** & 126 & 95 & 41 & 110 & 63 & 81 & 53 \\  & MCT (s) & 0.615 & 0.888 & 0.997 & **0.452** & 2995.907 & 114.868 & 87.382 & 79.007 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Detailed testing results of different algorithms on the dataset _Testing_R1_. that the increases in the total profit and the total task number are more significant than in the gained profit and the completion number, indicating that the problem scales exceed the observation capability of the AOS. 4. In general, DRLLA shows excellent performance in solution quality over the comparison algorithms. With the increase of task scale, the gap of the MPR values between DRLLA and the comparison algorithms becomes significant. In addition, the computational time of DRLLA is not less than that of RLPT, but its computation efficiency is superior to other comparison algorithms, especially heuristic algorithms. Thus, the comparison results on the dataset _Testing_R1_ fully demonstrate that DRLLA is capable of obtaining high-quality solutions within acceptable computational time. Figure 8: Profit ratio distributions of different algorithms on the dataset _Testing_R1_. Figure 9: Curves of mean profit and mean completion number. (**a**) Mean profit. (**b**) Mean completion number.