Python毕业设计基于深度强化学习的MEC计算卸载与资源分配源码（高分项目）.zip

Namespace(F=1, num_ue=5) Full_local 5.009379190666772 Full Offload 13.889811945013754 train epoch 0 avgSumCost = 5.094692665836453 train epoch 1 avgSumCost = 5.009379190666772 train epoch 2 avgSumCost = 5.009379190666772 train epoch 3 avgSumCost = 5.009379190666772 train epoch 4 avgSumCost = 5.009379190666772 train epoch 5 avgSumCost = 5.009379190666772 train epoch 6 avgSumCost = 5.009379190666772 train epoch 7 avgSumCost = 5.009379190666772 train epoch 8 avgSumCost = 5.009379190666772 train epoch 9 avgSumCost = 5.009379190666772 train epoch 10 avgSumCost = 5.009379190666772 train epoch 11 avgSumCost = 5.009379190666772 train epoch 12 avgSumCost = 5.009379190666772 train epoch 13 avgSumCost = 5.009379190666772 train epoch 14 avgSumCost = 5.009379190666772 train epoch 15 avgSumCost = 5.009379190666772 train epoch 16 avgSumCost = 5.009379190666772 train epoch 17 avgSumCost = 5.009379190666772 train epoch 18 avgSumCost = 5.009379190666772 train epoch 19 avgSumCost = 5.065912595923075 train epoch 20 avgSumCost = 5.009379190666772 train epoch 21 avgSumCost = 5.009379190666772 train epoch 22 avgSumCost = 5.009379190666772 train epoch 23 avgSumCost = 5.064339597147232 train epoch 24 avgSumCost = 5.009379190666772 train epoch 25 avgSumCost = 5.009379190666772 train epoch 26 avgSumCost = 5.009379190666772 train epoch 27 avgSumCost = 5.009379190666772 train epoch 28 avgSumCost = 5.009379190666772 train epoch 29 avgSumCost = 5.009379190666772 Q-learning 5.009379190666772 [5.094692665836453, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.065912595923075, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.064339597147232, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772, 5.009379190666772] Namespace(F=2, num_ue=5) Full_local 4.887548204914221 Full Offload 6.815699305350944 train epoch 0 avgSumCost = 4.5648808720213925 train epoch 1 avgSumCost = 4.667043444881105 train epoch 2 avgSumCost = 4.887548204914221 train epoch 3 avgSumCost = 4.887548204914221 train epoch 4 avgSumCost = 4.887548204914221 train epoch 5 avgSumCost = 4.887548204914221 train epoch 6 avgSumCost = 4.887548204914221 train epoch 7 avgSumCost = 4.887548204914221 train epoch 8 avgSumCost = 4.887548204914221 train epoch 9 avgSumCost = 4.667043444881105 train epoch 10 avgSumCost = 4.887548204914221 train epoch 11 avgSumCost = 4.887548204914221 train epoch 12 avgSumCost = 4.887548204914221 train epoch 13 avgSumCost = 4.142437067426093 train epoch 14 avgSumCost = 4.09384076737912 train epoch 15 avgSumCost = 4.08624583327997 train epoch 16 avgSumCost = 4.077816420513377 train epoch 17 avgSumCost = 4.077443502867611 train epoch 18 avgSumCost = 4.070790627130448 train epoch 19 avgSumCost = 4.071315886761339 train epoch 20 avgSumCost = 4.070576877540551 train epoch 21 avgSumCost = 4.071040335662298 train epoch 22 avgSumCost = 4.071900704581196 train epoch 23 avgSumCost = 4.072247679520573 train epoch 24 avgSumCost = 4.0726542747861805 train epoch 25 avgSumCost = 4.072451749055373 train epoch 26 avgSumCost = 4.386717119831153 train epoch 27 avgSumCost = 4.887548204914221 train epoch 28 avgSumCost = 4.071454468760273 train epoch 29 avgSumCost = 4.075417887437203 Q-learning 3.9822166954489373 [4.5648808720213925, 4.667043444881105, 4.887548204914221, 4.887548204914221, 4.887548204914221, 4.887548204914221, 4.887548204914221, 4.887548204914221, 4.887548204914221, 4.667043444881105, 4.887548204914221, 4.887548204914221, 4.887548204914221, 4.142437067426093, 4.09384076737912, 4.08624583327997, 4.077816420513377, 4.077443502867611, 4.070790627130448, 4.071315886761339, 4.070576877540551, 4.071040335662298, 4.071900704581196, 4.072247679520573, 4.0726542747861805, 4.072451749055373, 4.386717119831153, 4.887548204914221, 4.071454468760273, 4.075417887437203] Namespace(F=3, num_ue=5) Full_local 4.912044414348703 Full Offload 4.606105886930349 train epoch 0 avgSumCost = 3.632028224673733 train epoch 1 avgSumCost = 3.6214871756287694 train epoch 2 avgSumCost = 3.621898451830713 train epoch 3 avgSumCost = 3.6222909838266224 train epoch 4 avgSumCost = 3.6211538584489404 train epoch 5 avgSumCost = 3.62097907357618 train epoch 6 avgSumCost = 3.6214432926499804 train epoch 7 avgSumCost = 3.622168133917381 train epoch 8 avgSumCost = 3.621670053813848 train epoch 9 avgSumCost = 3.62134361684143 train epoch 10 avgSumCost = 3.621523989332771 train epoch 11 avgSumCost = 3.6219150036376204 train epoch 12 avgSumCost = 3.6219823910846074 train epoch 13 avgSumCost = 3.621937024613577 train epoch 14 avgSumCost = 3.652660912938653 train epoch 15 avgSumCost = 3.6458899078200355 train epoch 16 avgSumCost = 3.6215392241265993 train epoch 17 avgSumCost = 3.642733750790841 train epoch 18 avgSumCost = 3.6212515851673066 train epoch 19 avgSumCost = 3.6209295961142804 train epoch 20 avgSumCost = 3.649792728064822 train epoch 21 avgSumCost = 3.6194419794094044 train epoch 22 avgSumCost = 3.6206526997690234 train epoch 23 avgSumCost = 3.6172611674153146 train epoch 24 avgSumCost = 3.6247001008835373 train epoch 25 avgSumCost = 3.6237724287470634 train epoch 26 avgSumCost = 3.630732002473451 train epoch 27 avgSumCost = 3.6221393324859945 train epoch 28 avgSumCost = 3.615116878067054 train epoch 29 avgSumCost = 3.6233101071181544 Q-learning 3.595911306981166 [3.632028224673733, 3.6214871756287694, 3.621898451830713, 3.6222909838266224, 3.6211538584489404, 3.62097907357618, 3.6214432926499804, 3.622168133917381, 3.621670053813848, 3.62134361684143, 3.621523989332771, 3.6219150036376204, 3.6219823910846074, 3.621937024613577, 3.652660912938653, 3.6458899078200355, 3.6215392241265993, 3.642733750790841, 3.6212515851673066, 3.6209295961142804, 3.649792728064822, 3.6194419794094044, 3.6206526997690234, 3.6172611674153146, 3.6247001008835373, 3.6237724287470634, 3.630732002473451, 3.6221393324859945, 3.615116878067054, 3.6233101071181544] Namespace(F=4, num_ue=5) Full_local 4.789397153234043 Full Offload 3.3873070097886715 train epoch 0 avgSumCost = 3.358768307300043 train epoch 1 avgSumCost = 4.789397153234043 train epoch 2 avgSumCost = 4.789397153234043 train epoch 3 avgSumCost = 4.789397153234043 train epoch 4 avgSumCost = 4.789397153234043 train epoch 5 avgSumCost = 4.789397153234043 train epoch 6 avgSumCost = 4.789397153234043 train epoch 7 avgSumCost = 4.789397153234043 train epoch 8 avgSumCost = 4.521549710916772 train epoch 9 avgSumCost = 4.789397153234043 train epoch 10 avgSumCost = 4.789397153234043 train epoch 11 avgSumCost = 4.789397153234043 train epoch 12 avgSumCost = 4.789397153234043 train epoch 13 avgSumCost = 4.789397153234043 train epoch 14 avgSumCost = 4.789397153234043 train epoch 15 avgSumCost = 4.521549710916772 train epoch 16 avgSumCost = 4.789397153234043 train epoch 17 avgSumCost = 4.789397153234043 train epoch 18 avgSumCost = 4.521549710916772 train epoch 19 avgSumCost = 3.9816366517125052 train epoch 20 avgSumCost = 4.789397153234043 train epoch 21 avgSumCost = 4.789397153234043 train epoch 22 avgSumCost = 4.789397153234043 train epoch 23 avgSumCost = 4.789397153234043 train epoch 24 avgSumCost = 4.789397153234043 train epoch 25 avgSumCost = 4.789397153234043 train epoch 26 avgSumCost = 4.789397153234043 train epoch 27 avgSumCost = 4.789397153234043 train epoch 28 avgSumCost = 4.789397153234043 train epoch 29 avgSumCost = 4.789397153234043 Q-learning 3.0846977339557067 [3.358768307300043, 4.789397153234043, 4.789397153234043, 4.789397153234043, 4.789397153234043, 4.789397153234043, 4.789397153234043, 4.789397153