7720721 490543 0.00490 106 39 -0.00387 0.00710 284% 33.1 1100s
7748696 483817 0.00296 104 49 -0.00387 0.00699 281% 33.0 1105s
7780497 475976 cutoff 94 -0.00387 0.00685 277% 33.0 1110s
7811824 467988 -0.00133 100 51 -0.00387 0.00672 274% 33.0 1115s
7843151 460266 cutoff 113 -0.00387 0.00659 270% 33.0 1120s
7874613 452228 -0.00029 94 55 -0.00387 0.00646 267% 33.0 1125s
7903799 444712 cutoff 97 -0.00387 0.00633 264% 33.0 1130s
7930771 437563 cutoff 93 -0.00387 0.00621 260% 33.0 1135s
7962988 428875 cutoff 91 -0.00387 0.00607 257% 33.0 1140s
7994345 420675 cutoff 100 -0.00387 0.00593 253% 33.0 1145s
8023508 412871 cutoff 96 -0.00387 0.00580 250% 33.0 1150s
8054254 404584 -0.00099 96 53 -0.00387 0.00566 246% 32.9 1155s
8085966 395686 -0.00197 101 49 -0.00387 0.00551 243% 32.9 1160s
8117492 386811 cutoff 102 -0.00387 0.00537 239% 32.9 1165s
8148869 377804 cutoff 86 -0.00387 0.00522 235% 32.9 1170s
8180191 368355 -0.00191 109 48 -0.00387 0.00506 231% 32.9 1175s
8207487 360257 cutoff 90 -0.00387 0.00493 227% 32.9 1180s
8239687 350820 0.00213 110 41 -0.00387 0.00477 223% 32.9 1185s
8268924 341879 cutoff 106 -0.00387 0.00463 220% 32.9 1190s
8302562 331745 0.00355 98 56 -0.00387 0.00446 215% 32.8 1195s
8330567 322821 cutoff 104 -0.00387 0.00432 212% 32.8 1200s
8362214 312837 cutoff 103 -0.00387 0.00415 207% 32.8 1205s
8393739 301860 0.00232 108 42 -0.00387 0.00398 203% 32.8 1210s
8425312 291141 -0.00014 111 41 -0.00387 0.00380 198% 32.8 1215s
8454774 280963 cutoff 114 -0.00387 0.00364 194% 32.8 1220s
8483011 270952 cutoff 112 -0.00387 0.00347 190% 32.8 1225s
8514807 259639 0.00075 101 45 -0.00387 0.00328 185% 32.7 1230s
8546277 248131 -0.00076 121 36 -0.00387 0.00309 180% 32.7 1235s
8575538 237123 -0.00120 100 50 -0.00387 0.00291 175% 32.7 1240s
8607118 224732 -0.00231 91 50 -0.00387 0.00270 170% 32.7 1245s
8638730 211961 -0.00016 100 53 -0.00387 0.00248 164% 32.7 1250s
8670405 199002 cutoff 106 -0.00387 0.00226 158% 32.7 1255s
8697816 187057 cutoff 108 -0.00387 0.00205 153% 32.6 1260s
8731097 172179 -0.00279 104 38 -0.00387 0.00178 146% 32.6 1265s
8759508 159108 cutoff 103 -0.00387 0.00154 140% 32.6 1270s
8791180 143796 -0.00288 109 46 -0.00387 0.00125 132% 32.6 1275s
8823154 127552 cutoff 110 -0.00387 0.00094 124% 32.6 1280s
8856874 109809 -0.00041 105 45 -0.00387 0.00057 115% 32.5 1285s
8890858 90140 -0.00315 116 45 -0.00387 0.00013 103% 32.5 1290s
8924577 69220 cutoff 99 -0.00387 -0.00039 90.0% 32.5 1295s
8960768 43861 cutoff 112 -0.00387 -0.00113 70.7% 32.4 1300s
8996308 14799 cutoff 118 -0.00387 -0.00238 38.5% 32.4 1305s
Cutting planes:
Gomory: 26
Cover: 4
MIR: 31
Flow cover: 32
Inf proof: 14
RLT: 69
Relax-and-lift: 2
Explored 9014016 nodes (291488759 simplex iterations) in 1306.51 seconds (2297.25 work units)
Thread count was 20 (of 20 available processors)
Solution count 10: -0.00386871 -0.00452073 -0.00471359 ... -0.0113476
No other solutions better than -0.00386871
Optimal solution found (tolerance 1.00e-04)
Best objective -3.868711006243e-03, best bound -3.868711006243e-03, gap 0.0000%
Read LP format model from file C:\Users\A\AppData\Local\Temp\tmpobhw7kgm.pyomo.lp
Reading time = 0.01 seconds
x1: 988 rows, 606 columns, 10740 nonzeros
Gurobi Optimizer version 12.0.1 build v12.0.1rc0 (win64 - Windows 11+.0 (26200.2))
CPU model: 13th Gen Intel(R) Core(TM) i5-13600KF, instruction set [SSE2|AVX|AVX2]
Thread count: 14 physical cores, 20 logical processors, using up to 20 threads
Optimize a model with 988 rows, 606 columns and 10740 nonzeros
Model fingerprint: 0x6027cab4
Variable types: 414 continuous, 192 integer (192 binary)
Coefficient statistics:
Matrix range [2e-05, 2e+02]
Objective range [1e+00, 1e+00]
Bounds range [8e-02, 1e+01]
RHS range [1e-03, 1e+02]
Presolve removed 319 rows and 97 columns
Presolve time: 0.03s
Presolved: 669 rows, 509 columns, 9115 nonzeros
Variable types: 347 continuous, 162 integer (162 binary)
Root relaxation: objective 1.086856e+00, 420 iterations, 0.01 seconds (0.02 work units)
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
0 0 1.08686 0 90 - 1.08686 - - 0s
H 0 0 -0.0075947 1.08652 - - 0s
0 0 0.99803 0 106 -0.00759 0.99803 - - 0s
0 0 0.96350 0 117 -0.00759 0.96350 - - 0s
0 0 0.93538 0 121 -0.00759 0.93538 - - 0s
0 0 0.92896 0 125 -0.00759 0.92896 - - 0s
0 0 0.89033 0 129 -0.00759 0.89033 - - 0s
0 0 0.88584 0 132 -0.00759 0.88584 - - 0s
0 0 0.88546 0 131 -0.00759 0.88546 - - 0s
0 0 0.88513 0 132 -0.00759 0.88513 - - 0s
0 0 0.88493 0 130 -0.00759 0.88493 - - 0s
0 0 0.88479 0 130 -0.00759 0.88479 - - 0s
0 0 0.88467 0 131 -0.00759 0.88467 - - 0s
0 0 0.88465 0 131 -0.00759 0.88465 - - 0s
0 0 0.85952 0 128 -0.00759 0.85952 - - 0s
0 0 0.85447 0 128 -0.00759 0.85447 - - 0s
0 0 0.85276 0 127 -0.00759 0.85276 - - 0s
0 0 0.85251 0 128 -0.00759 0.85251 - - 0s
0 0 0.84683 0 125 -0.00759 0.84683 - - 0s
0 0 0.84623 0 126 -0.00759 0.84623 - - 0s
0 0 0.84597 0 126 -0.00759 0.84597 - - 0s
0 0 0.84594 0 125 -0.00759 0.84594 - - 0s
0 0 0.80962 0 123 -0.00759 0.80962 - - 0s
0 0 0.80552 0 122 -0.00759 0.80552 - - 0s
0 0 0.80552 0 125 -0.00759 0.80552 - - 0s
0 0 0.80552 0 125 -0.00759 0.80552 - - 0s
0 0 0.80552 0 126 -0.00759 0.80552 - - 0s
0 0 0.78430 0 122 -0.00759 0.78430 - - 1s
0 0 0.78178 0 124 -0.00759 0.78178 - - 1s
0 0 0.78072 0 122 -0.00759 0.78072 - - 1s
0 0 0.77990 0 123 -0.00759 0.77990 - - 1s
0 0 0.77970 0 123 -0.00759 0.77970 - - 1s
0 0 0.76946 0 127 -0.00759 0.76946 - - 1s
0 0 0.76553 0 125 -0.00759 0.76553 - - 1s
0 0 0.76386 0 127 -0.00759 0.76386 - - 1s
0 0 0.76351 0 127 -0.00759 0.76351 - - 1s
0 0 0.76342 0 126 -0.00759 0.76342 - - 1s
0 0 0.75405 0 129 -0.00759 0.75405 - - 1s
0 0 0.75285 0 130 -0.00759 0.75285 - - 1s
0 0 0.75218 0 131 -0.00759 0.75218 - - 1s
0 0 0.75210 0 130 -0.00759 0.75210 - - 1s
0 0 0.74645 0 129 -0.00759 0.74645 9929% - 1s
0 0 0.74406 0 129 -0.00759 0.74406 9897% - 1s
0 0 0.74335 0 129 -0.00759 0.74335 9888% - 1s
0 0 0.74283 0 128 -0.00759 0.74283 9881% - 1s
0 0 0.74265 0 131 -0.00759 0.74265 9879% - 1s
0 0 0.73723 0 130 -0.00759 0.73723 9807% - 1s
0 0 0.73620 0 131 -0.00759 0.73620 9794% - 1s
0 0 0.73601 0 129 -0.00759 0.73601 9791% - 1s
0 0 0.73422 0 131 -0.00759 0.73422 9768% - 1s
0 0 0.73235 0 131 -0.00759 0.73235 9743% - 1s
0 0 0.73097 0 129 -0.00759 0.73097 9725% - 1s
0 0 0.73066 0 129 -0.00759 0.73066 9721% - 1s
0 0 0.73033 0 128 -0.00759 0.73033 9716% - 1s
0 0 0.73010 0 128 -0.00759 0.73010 9713% - 1s
0 0 0.72584 0 128 -0.00759 0.72584 9657% - 1s
0 0 0.72476 0 129 -0.00759 0.72476 9643% - 1s
0 0 0.72435 0 129 -0.00759 0.72435 9638% - 1s
0 0 0.72392 0 130 -0.00759 0.72392 9632% - 1s
0 0 0.72368 0 130 -0.00759 0.72368 9629% - 2s
0 0 0.72204 0 129 -0.00759 0.72204 9607% - 2s
0 0 0.72099 0 128 -0.00759 0.72099 9593% - 2s
0 0 0.72047 0 131 -0.00759 0.72047 9587% - 2s
0 0 0.71975 0 131 -0.00759 0.71975 9577% - 2s
0 0 0.71959 0 131 -0.00759 0.71959 9575% - 2s
0 0 0.70824 0 130 -0.00759 0.70824 9425% - 2s
0 0 0.70693 0 131 -0.00759 0.70693 9408% - 2s
0 0 0.70671 0 130 -0.00759 0.70671 9405% - 2s
0 0 0.70617 0 130 -0.00759 0.70617 9398% - 2s
0 0 0.69477 0 126 -0.00759 0.69477 9248% - 2s
0 2 0.69359 0 126 -0.00759 0.69359 9233% - 2s
H 82 93 -0.0075946 0.61181 8156% 120 3s
2435 1652 0.22707 17 102 -0.00759 0.55725 7437% 49.7 5s
H 2469 1591 0.0072465 0.35936 4859% 50.1 8s
2480 1599 0.17862 12 71 0.00725 0.17862 2365% 51.1 10s
H 2575 1568 0.0072465 0.16516 2179% 52.2 11s
H 3632 1628 0.0074209 0.13596 1732% 48.4 11s
H 3634 1555 0.0074361 0.13596 1728% 48.4 11s
H 4280 1632 0.0075486 0.13307 1663% 45.5 12s
- 6860 2252 107 0.0079808 0.11728 1369% 40.6 12s
13706 4656 cutoff 53 0.00798 0.09066 1036% 37.0 15s
H16789 5533 0.0079811 0.08245 933% 35.6 15s
46640 15267 0.02407 60 54 0.00798 0.04791 500% 29.9 25s
79602 19751 0.01732 62 53 0.00798 0.03526 342% 32.1 30s
96531 19312 0.01061 62 58 0.00798 0.03055 283% 32.8 37s
117018 18864 0.02169 66 46 0.00798 0.02717 240% 32.7 40s
151161 16057 0.01259 62 58 0.00798 0.02076 160% 32.5 45s
184704 8882 0.00835 67 53 0.00798 0.01472 84.5% 31.5 50s
*195535 6099 104 0.0082361 0.01284 55.9% 30.8 51s
Cutting planes:
Gomory: 21
Cover: 1
Implied bound: 1
MIR: 48
Flow cover: 25
Inf proof: 3
RLT: 70
Relax-and-lift: 5
BQP: 3
Explored 206807 nodes (6195431 simplex iterations) in 52.54 seconds (97.75 work units)
Thread count was 20 (of 20 available processors)
Solution count 9: 0.00823606 0.00798108 0.00798085 ... -0.00759465
No other solutions better than 0.00823606
Optimal solution found (tolerance 1.00e-04)
Best objective 8.236060664135e-03, best bound 8.236060664135e-03, gap 0.0000%
Read LP format model from file C:\Users\A\AppData\Local\Temp\tmpvl8org5j.pyomo.lp
Reading time = 0.01 seconds
x1: 988 rows, 606 columns, 10740 nonzeros
Gurobi Optimizer version 12.0.1 build v12.0.1rc0 (win64 - Windows 11+.0 (26200.2))
CPU model: 13th Gen Intel(R) Core(TM) i5-13600KF, instruction set [SSE2|AVX|AVX2]
Thread count: 14 physical cores, 20 logical processors, using up to 20 threads
Optimize a model with 988 rows, 606 columns and 10740 nonzeros
Model fingerprint: 0xeffb8fb4
Variable types: 414 continuous, 192 integer (192 binary)
Coefficient statistics:
Matrix range [2e-05, 2e+02]
Objective range [1e+00, 1e+00]
Bounds range [6e-02, 1e+01]
RHS range [1e-03, 2e+02]
Presolve removed 197 rows and 4 columns
Presolve time: 0.00s
Explored 0 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units)
Thread count was 1 (of 20 available processors)
Solution count 0
Model is infeasible or unbounded
Best objective -, best bound -, gap -
WARNING: Loading a SolverResults object with a warning status into
model.name="unknown";
- termination condition: infeasibleOrUnbounded
- message from solver: Problem proven to be infeasible or unbounded.
Traceback (most recent call last):
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 823, in
main()
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 819, in main
train_one_seed(seed, args)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 762, in train_one_seed
collect_trajectory(agent, env, buffer, args, actor_mip)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 698, in collect_trajectory
trajectory = agent.explore_env(env, args.target_step, actor_mip=actor_mip)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 267, in explore_env
action = self.select_action(state, actor_mip=actor_mip, use_exploration=True)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 260, in select_action
return actor_mip.project_action(state, raw_action)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 641, in project_action
model = self._solve_current_model(state, actor_action)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 634, in _solve_current_model
raise RuntimeError(
RuntimeError: Actor_MIP solve failed with status=warning, termination=infeasibleOrUnbounded, state=[0.08695652335882187, 0.12852515280246735, 0.8633788228034973, 0.024901989847421646, 0.7173611521720886, 0.0, 0.2574736177921295], actor_action=[-0.11503194272518158, 0.9105579853057861, -0.35117125511169434, 0.6947029829025269].
Traceback (most recent call last):
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 823, in
main()
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 819, in main
train_one_seed(seed, args)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 762, in train_one_seed
collect_trajectory(agent, env, buffer, args, actor_mip)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 698, in collect_trajectory
trajectory = agent.explore_env(env, args.target_step, actor_mip=actor_mip)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 267, in explore_env
action = self.select_action(state, actor_mip=actor_mip, use_exploration=True)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 260, in select_action
return actor_mip.project_action(state, raw_action)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 641, in project_action
model = self._solve_current_model(state, actor_action)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 634, in _solve_current_model
raise RuntimeError(
RuntimeError: Actor_MIP solve failed with status=warning, termination=infeasibleOrUnbounded, state=[0.08695652335882187, 0.12852515280246735, 0.8633788228034973, 0.024901989847421646, 0.7173611521720886, 0.0, 0.2574736177921295], actor_action=[-0.11503194272518158, 0.9105579853057861, -0.35117125511169434, 0.6947029829025269].
wandb: You can sync this run to the cloud by running:
wandb: wandb sync D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\wandb\offline-run-20260405_131336-cygnojes
wandb: Find logs at: .\wandb\offline-run-20260405_131336-cygnojes\logs
运行时报错，前两次都能求解，为什么第三回就报错呢？
7720721 490543 0.00490 106 39 -0.00387 0.00710 284% 33.1 1100s
7748696 483817 0.00296 104 49 -0.00387 0.00699 281% 33.0 1105s
7780497 475976 cutoff 94 -0.00387 0.00685 277% 33.0 1110s
7811824 467988 -0.00133 100 51 -0.00387 0.00672 274% 33.0 1115s
7843151 460266 cutoff 113 -0.00387 0.00659 270% 33.0 1120s
7874613 452228 -0.00029 94 55 -0.00387 0.00646 267% 33.0 1125s
7903799 444712 cutoff 97 -0.00387 0.00633 264% 33.0 1130s
7930771 437563 cutoff 93 -0.00387 0.00621 260% 33.0 1135s
7962988 428875 cutoff 91 -0.00387 0.00607 257% 33.0 1140s
7994345 420675 cutoff 100 -0.00387 0.00593 253% 33.0 1145s
8023508 412871 cutoff 96 -0.00387 0.00580 250% 33.0 1150s
8054254 404584 -0.00099 96 53 -0.00387 0.00566 246% 32.9 1155s
8085966 395686 -0.00197 101 49 -0.00387 0.00551 243% 32.9 1160s
8117492 386811 cutoff 102 -0.00387 0.00537 239% 32.9 1165s
8148869 377804 cutoff 86 -0.00387 0.00522 235% 32.9 1170s
8180191 368355 -0.00191 109 48 -0.00387 0.00506 231% 32.9 1175s
8207487 360257 cutoff 90 -0.00387 0.00493 227% 32.9 1180s
8239687 350820 0.00213 110 41 -0.00387 0.00477 223% 32.9 1185s
8268924 341879 cutoff 106 -0.00387 0.00463 220% 32.9 1190s
8302562 331745 0.00355 98 56 -0.00387 0.00446 215% 32.8 1195s
8330567 322821 cutoff 104 -0.00387 0.00432 212% 32.8 1200s
8362214 312837 cutoff 103 -0.00387 0.00415 207% 32.8 1205s
8393739 301860 0.00232 108 42 -0.00387 0.00398 203% 32.8 1210s
8425312 291141 -0.00014 111 41 -0.00387 0.00380 198% 32.8 1215s
8454774 280963 cutoff 114 -0.00387 0.00364 194% 32.8 1220s
8483011 270952 cutoff 112 -0.00387 0.00347 190% 32.8 1225s
8514807 259639 0.00075 101 45 -0.00387 0.00328 185% 32.7 1230s
8546277 248131 -0.00076 121 36 -0.00387 0.00309 180% 32.7 1235s
8575538 237123 -0.00120 100 50 -0.00387 0.00291 175% 32.7 1240s
8607118 224732 -0.00231 91 50 -0.00387 0.00270 170% 32.7 1245s
8638730 211961 -0.00016 100 53 -0.00387 0.00248 164% 32.7 1250s
8670405 199002 cutoff 106 -0.00387 0.00226 158% 32.7 1255s
8697816 187057 cutoff 108 -0.00387 0.00205 153% 32.6 1260s
8731097 172179 -0.00279 104 38 -0.00387 0.00178 146% 32.6 1265s
8759508 159108 cutoff 103 -0.00387 0.00154 140% 32.6 1270s
8791180 143796 -0.00288 109 46 -0.00387 0.00125 132% 32.6 1275s
8823154 127552 cutoff 110 -0.00387 0.00094 124% 32.6 1280s
8856874 109809 -0.00041 105 45 -0.00387 0.00057 115% 32.5 1285s
8890858 90140 -0.00315 116 45 -0.00387 0.00013 103% 32.5 1290s
8924577 69220 cutoff 99 -0.00387 -0.00039 90.0% 32.5 1295s
8960768 43861 cutoff 112 -0.00387 -0.00113 70.7% 32.4 1300s
8996308 14799 cutoff 118 -0.00387 -0.00238 38.5% 32.4 1305s
Cutting planes:
Gomory: 26
Cover: 4
MIR: 31
Flow cover: 32
Inf proof: 14
RLT: 69
Relax-and-lift: 2
Explored 9014016 nodes (291488759 simplex iterations) in 1306.51 seconds (2297.25 work units)
Thread count was 20 (of 20 available processors)
Solution count 10: -0.00386871 -0.00452073 -0.00471359 ... -0.0113476
No other solutions better than -0.00386871
Optimal solution found (tolerance 1.00e-04)
Best objective -3.868711006243e-03, best bound -3.868711006243e-03, gap 0.0000%
Read LP format model from file C:\Users\A\AppData\Local\Temp\tmpobhw7kgm.pyomo.lp
Reading time = 0.01 seconds
x1: 988 rows, 606 columns, 10740 nonzeros
Gurobi Optimizer version 12.0.1 build v12.0.1rc0 (win64 - Windows 11+.0 (26200.2))
CPU model: 13th Gen Intel(R) Core(TM) i5-13600KF, instruction set [SSE2|AVX|AVX2]
Thread count: 14 physical cores, 20 logical processors, using up to 20 threads
Optimize a model with 988 rows, 606 columns and 10740 nonzeros
Model fingerprint: 0x6027cab4
Variable types: 414 continuous, 192 integer (192 binary)
Coefficient statistics:
Matrix range [2e-05, 2e+02]
Objective range [1e+00, 1e+00]
Bounds range [8e-02, 1e+01]
RHS range [1e-03, 1e+02]
Presolve removed 319 rows and 97 columns
Presolve time: 0.03s
Presolved: 669 rows, 509 columns, 9115 nonzeros
Variable types: 347 continuous, 162 integer (162 binary)
Root relaxation: objective 1.086856e+00, 420 iterations, 0.01 seconds (0.02 work units)
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
H 0 0 -0.0075947 1.08652 - - 0s
0 0 0.99803 0 106 -0.00759 0.99803 - - 0s
0 0 0.96350 0 117 -0.00759 0.96350 - - 0s
0 0 0.93538 0 121 -0.00759 0.93538 - - 0s
0 0 0.92896 0 125 -0.00759 0.92896 - - 0s
0 0 0.89033 0 129 -0.00759 0.89033 - - 0s
0 0 0.88584 0 132 -0.00759 0.88584 - - 0s
0 0 0.88546 0 131 -0.00759 0.88546 - - 0s
0 0 0.88513 0 132 -0.00759 0.88513 - - 0s
0 0 0.88493 0 130 -0.00759 0.88493 - - 0s
0 0 0.88479 0 130 -0.00759 0.88479 - - 0s
0 0 0.88467 0 131 -0.00759 0.88467 - - 0s
0 0 0.88465 0 131 -0.00759 0.88465 - - 0s
0 0 0.85952 0 128 -0.00759 0.85952 - - 0s
0 0 0.85447 0 128 -0.00759 0.85447 - - 0s
0 0 0.85276 0 127 -0.00759 0.85276 - - 0s
0 0 0.85251 0 128 -0.00759 0.85251 - - 0s
0 0 0.84683 0 125 -0.00759 0.84683 - - 0s
0 0 0.84623 0 126 -0.00759 0.84623 - - 0s
0 0 0.84597 0 126 -0.00759 0.84597 - - 0s
0 0 0.84594 0 125 -0.00759 0.84594 - - 0s
0 0 0.80962 0 123 -0.00759 0.80962 - - 0s
0 0 0.80552 0 122 -0.00759 0.80552 - - 0s
0 0 0.80552 0 125 -0.00759 0.80552 - - 0s
0 0 0.80552 0 125 -0.00759 0.80552 - - 0s
0 0 0.80552 0 126 -0.00759 0.80552 - - 0s
0 0 0.78430 0 122 -0.00759 0.78430 - - 1s
0 0 0.78178 0 124 -0.00759 0.78178 - - 1s
0 0 0.78072 0 122 -0.00759 0.78072 - - 1s
0 0 0.77990 0 123 -0.00759 0.77990 - - 1s
0 0 0.77970 0 123 -0.00759 0.77970 - - 1s
0 0 0.76946 0 127 -0.00759 0.76946 - - 1s
0 0 0.76553 0 125 -0.00759 0.76553 - - 1s
0 0 0.76386 0 127 -0.00759 0.76386 - - 1s
0 0 0.76351 0 127 -0.00759 0.76351 - - 1s
0 0 0.76342 0 126 -0.00759 0.76342 - - 1s
0 0 0.75405 0 129 -0.00759 0.75405 - - 1s
0 0 0.75285 0 130 -0.00759 0.75285 - - 1s
0 0 0.75218 0 131 -0.00759 0.75218 - - 1s
0 0 0.75210 0 130 -0.00759 0.75210 - - 1s
0 0 0.74645 0 129 -0.00759 0.74645 9929% - 1s
0 0 0.74406 0 129 -0.00759 0.74406 9897% - 1s
0 0 0.74335 0 129 -0.00759 0.74335 9888% - 1s
0 0 0.74283 0 128 -0.00759 0.74283 9881% - 1s
0 0 0.74265 0 131 -0.00759 0.74265 9879% - 1s
0 0 0.73723 0 130 -0.00759 0.73723 9807% - 1s
0 0 0.73620 0 131 -0.00759 0.73620 9794% - 1s
0 0 0.73601 0 129 -0.00759 0.73601 9791% - 1s
0 0 0.73422 0 131 -0.00759 0.73422 9768% - 1s
0 0 0.73235 0 131 -0.00759 0.73235 9743% - 1s
0 0 0.73097 0 129 -0.00759 0.73097 9725% - 1s
0 0 0.73066 0 129 -0.00759 0.73066 9721% - 1s
0 0 0.73033 0 128 -0.00759 0.73033 9716% - 1s
0 0 0.73010 0 128 -0.00759 0.73010 9713% - 1s
0 0 0.72584 0 128 -0.00759 0.72584 9657% - 1s
0 0 0.72476 0 129 -0.00759 0.72476 9643% - 1s
0 0 0.72435 0 129 -0.00759 0.72435 9638% - 1s
0 0 0.72392 0 130 -0.00759 0.72392 9632% - 1s
0 0 0.72368 0 130 -0.00759 0.72368 9629% - 2s
0 0 0.72204 0 129 -0.00759 0.72204 9607% - 2s
0 0 0.72099 0 128 -0.00759 0.72099 9593% - 2s
0 0 0.72047 0 131 -0.00759 0.72047 9587% - 2s
0 0 0.71975 0 131 -0.00759 0.71975 9577% - 2s
0 0 0.71959 0 131 -0.00759 0.71959 9575% - 2s
0 0 0.70824 0 130 -0.00759 0.70824 9425% - 2s
0 0 0.70693 0 131 -0.00759 0.70693 9408% - 2s
0 0 0.70671 0 130 -0.00759 0.70671 9405% - 2s
0 0 0.70617 0 130 -0.00759 0.70617 9398% - 2s
0 0 0.69477 0 126 -0.00759 0.69477 9248% - 2s
0 2 0.69359 0 126 -0.00759 0.69359 9233% - 2s
H 82 93 -0.0075946 0.61181 8156% 120 3s
2435 1652 0.22707 17 102 -0.00759 0.55725 7437% 49.7 5s
H 2469 1591 0.0072465 0.35936 4859% 50.1 8s
2480 1599 0.17862 12 71 0.00725 0.17862 2365% 51.1 10s
H 2575 1568 0.0072465 0.16516 2179% 52.2 11s
H 3632 1628 0.0074209 0.13596 1732% 48.4 11s
H 3634 1555 0.0074361 0.13596 1728% 48.4 11s
H 4280 1632 0.0075486 0.13307 1663% 45.5 12s
13706 4656 cutoff 53 0.00798 0.09066 1036% 37.0 15s
H16789 5533 0.0079811 0.08245 933% 35.6 15s
46640 15267 0.02407 60 54 0.00798 0.04791 500% 29.9 25s
79602 19751 0.01732 62 53 0.00798 0.03526 342% 32.1 30s
96531 19312 0.01061 62 58 0.00798 0.03055 283% 32.8 37s
117018 18864 0.02169 66 46 0.00798 0.02717 240% 32.7 40s
151161 16057 0.01259 62 58 0.00798 0.02076 160% 32.5 45s
184704 8882 0.00835 67 53 0.00798 0.01472 84.5% 31.5 50s
*195535 6099 104 0.0082361 0.01284 55.9% 30.8 51s
Cutting planes:
Gomory: 21
Cover: 1
Implied bound: 1
MIR: 48
Flow cover: 25
Inf proof: 3
RLT: 70
Relax-and-lift: 5
BQP: 3
Explored 206807 nodes (6195431 simplex iterations) in 52.54 seconds (97.75 work units)
Thread count was 20 (of 20 available processors)
Solution count 9: 0.00823606 0.00798108 0.00798085 ... -0.00759465
No other solutions better than 0.00823606
Optimal solution found (tolerance 1.00e-04)
Best objective 8.236060664135e-03, best bound 8.236060664135e-03, gap 0.0000%
Read LP format model from file C:\Users\A\AppData\Local\Temp\tmpvl8org5j.pyomo.lp
Reading time = 0.01 seconds
x1: 988 rows, 606 columns, 10740 nonzeros
Gurobi Optimizer version 12.0.1 build v12.0.1rc0 (win64 - Windows 11+.0 (26200.2))
CPU model: 13th Gen Intel(R) Core(TM) i5-13600KF, instruction set [SSE2|AVX|AVX2]
Thread count: 14 physical cores, 20 logical processors, using up to 20 threads
Optimize a model with 988 rows, 606 columns and 10740 nonzeros
Model fingerprint: 0xeffb8fb4
Variable types: 414 continuous, 192 integer (192 binary)
Coefficient statistics:
Matrix range [2e-05, 2e+02]
Objective range [1e+00, 1e+00]
Bounds range [6e-02, 1e+01]
RHS range [1e-03, 2e+02]
Presolve removed 197 rows and 4 columns
Presolve time: 0.00s
Explored 0 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units)
Thread count was 1 (of 20 available processors)
Solution count 0
Model is infeasible or unbounded
Best objective -, best bound -, gap -
WARNING: Loading a SolverResults object with a warning status into
model.name="unknown";
- termination condition: infeasibleOrUnbounded
- message from solver: Problem proven to be infeasible or unbounded.
Traceback (most recent call last):
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 823, in
main()
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 819, in main
train_one_seed(seed, args)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 762, in train_one_seed
collect_trajectory(agent, env, buffer, args, actor_mip)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 698, in collect_trajectory
trajectory = agent.explore_env(env, args.target_step, actor_mip=actor_mip)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 267, in explore_env
action = self.select_action(state, actor_mip=actor_mip, use_exploration=True)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 260, in select_action
return actor_mip.project_action(state, raw_action)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 641, in project_action
model = self._solve_current_model(state, actor_action)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 634, in _solve_current_model
raise RuntimeError(
RuntimeError: Actor_MIP solve failed with status=warning, termination=infeasibleOrUnbounded, state=[0.08695652335882187, 0.12852515280246735, 0.8633788228034973, 0.024901989847421646, 0.7173611521720886, 0.0, 0.2574736177921295], actor_action=[-0.11503194272518158, 0.9105579853057861, -0.35117125511169434, 0.6947029829025269].
Traceback (most recent call last):
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 823, in
main()
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 819, in main
train_one_seed(seed, args)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 762, in train_one_seed
collect_trajectory(agent, env, buffer, args, actor_mip)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 698, in collect_trajectory
trajectory = agent.explore_env(env, args.target_step, actor_mip=actor_mip)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 267, in explore_env
action = self.select_action(state, actor_mip=actor_mip, use_exploration=True)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 260, in select_action
return actor_mip.project_action(state, raw_action)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 641, in project_action
model = self._solve_current_model(state, actor_action)
File "D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\MIP_DQN.py", line 634, in _solve_current_model
raise RuntimeError(
RuntimeError: Actor_MIP solve failed with status=warning, termination=infeasibleOrUnbounded, state=[0.08695652335882187, 0.12852515280246735, 0.8633788228034973, 0.024901989847421646, 0.7173611521720886, 0.0, 0.2574736177921295], actor_action=[-0.11503194272518158, 0.9105579853057861, -0.35117125511169434, 0.6947029829025269].
wandb: You can sync this run to the cloud by running:
wandb: wandb sync D:\Desktop\Optimal-Energy-System-Scheduling-Combining-Mixed-Integer-Programming-and-Deep-Reinforcement-Learning-main\wandb\offline-run-20260405_131336-cygnojes
wandb: Find logs at: .\wandb\offline-run-20260405_131336-cygnojes\logs
运行时报错，前两次都能求解，为什么第三回就报错呢？