📝 update the result of sycamore-n53 m12, m14, m16, m20
Yonv1943 opened this issue · comments
result: 以下的结果都需要+log10(2) ，可以参考以下讨论：
#102 (comment)
| sycamore | Result1 | Result2 | NumSamples | UsedTime |
|---|---|---|---|---|
| n53 m12 | 15.478 | 16.449 | 185856 | 56930 |
| n53 m14 | 16.610 | 17.748 | 173568 | 54152 |
| n53 m16 | 22.014 | 32.511 | 153088 | 52947 |
| n53 m18 | ||||
| n53 m20 | 21.782 | 22.585 | 148992 | 58058 |
sycamore n53 m12
228 22.789 2.196e+01 TimeUsed 54169
232 24.986 2.228e+01 TimeUsed 55085
236 25.889 2.051e+01 TimeUsed 56010
240 20.804 1.967e+01 TimeUsed 56930
| buffer.save_or_load_history(): Save ./task_TNCO_00/replay_buffer_states.pth torch.Size([185856, 414])
| buffer.save_or_load_history(): Save ./task_TNCO_00/replay_buffer_scores.pth torch.Size([185856, 1])
min_score: 15.478
avg_score: 24.392 ± 4.663
max_score: 49.068
best_result:
tensor([235, 371, 215, 137, 246, 62, 320, 178, 147, 325, 31, 17, 274, 333,
234, 389, 142, 49, 311, 351, 271, 218, 89, 121, 96, 401, 25, 230,
8, 369, 350, 257, 318, 248, 229, 236, 52, 14, 292, 139, 383, 343,
207, 195, 209, 47, 394, 355, 329, 149, 53, 130, 372, 398, 273, 339,
278, 314, 298, 28, 206, 98, 384, 74, 217, 256, 65, 134, 354, 323,
167, 166, 390, 151, 190, 182, 382, 367, 128, 18, 10, 408, 321, 119,
344, 100, 199, 120, 181, 405, 179, 288, 411, 140, 330, 305, 264, 336,
208, 356, 168, 60, 266, 348, 242, 59, 268, 397, 214, 243, 143, 263,
270, 87, 388, 125, 29, 204, 5, 16, 191, 282, 118, 322, 81, 104,
211, 228, 34, 296, 76, 152, 114, 392, 406, 24, 171, 244, 116, 306,
359, 138, 362, 338, 290, 227, 12, 308, 172, 237, 379, 197, 254, 259,
146, 176, 252, 366, 332, 22, 43, 216, 99, 79, 275, 196, 67, 258,
342, 294, 373, 198, 56, 283, 108, 346, 64, 175, 88, 102, 27, 135,
123, 357, 324, 19, 7, 55, 319, 162, 9, 349, 193, 41, 192, 155,
412, 352, 72, 186, 69, 160, 386, 267, 150, 312, 205, 20, 309, 364,
272, 164, 82, 23, 33, 358, 68, 107, 345, 285, 226, 95, 85, 145,
284, 94, 38, 233, 180, 378, 2, 42, 303, 300, 387, 360, 327, 91,
32, 109, 240, 260, 287, 115, 184, 86, 249, 21, 203, 75, 78, 341,
317, 286, 1, 276, 253, 131, 251, 241, 328, 36, 315, 310, 110, 11,
245, 37, 377, 381, 40, 307, 297, 4, 158, 289, 83, 188, 111, 293,
337, 396, 361, 280, 50, 380, 368, 84, 51, 54, 340, 212, 370, 169,
154, 326, 385, 255, 44, 201, 232, 103, 353, 409, 262, 156, 291, 101,
250, 200, 247, 80, 105, 194, 113, 157, 402, 265, 159, 174, 57, 133,
141, 185, 58, 238, 106, 334, 129, 136, 313, 127, 3, 365, 61, 277,
73, 92, 391, 46, 71, 213, 231, 26, 399, 144, 210, 304, 375, 374,
148, 269, 331, 30, 13, 410, 363, 165, 400, 316, 124, 222, 93, 153,
117, 39, 70, 170, 6, 132, 77, 224, 66, 63, 48, 239, 413, 302,
376, 219, 45, 126, 173, 221, 223, 183, 404, 177, 97, 279, 187, 407,
189, 15, 281, 395, 393, 403, 301, 112, 35, 90, 295, 225, 299, 0,
163, 261, 220, 122, 202, 161, 335, 347], device='cuda:0')
180 18.700 1.616e+01 TimeUsed 48409
184 19.871 1.838e+01 TimeUsed 49428
188 21.397 1.973e+01 TimeUsed 50447
192 24.096 1.993e+01 TimeUsed 51460
| buffer.save_or_load_history(): Save ./task_TNCO_04/replay_buffer_states.pth torch.Size([165376, 414])
| buffer.save_or_load_history(): Save ./task_TNCO_04/replay_buffer_scores.pth torch.Size([165376, 1])
min_score: 16.449
avg_score: 25.135 ± 5.301
max_score: 49.670
best_result:
tensor([396, 88, 166, 366, 408, 81, 167, 51, 243, 238, 148, 90, 5, 222,
159, 305, 361, 198, 22, 295, 321, 128, 339, 310, 169, 219, 375, 224,
409, 372, 38, 241, 247, 146, 338, 200, 36, 52, 54, 178, 226, 234,
173, 192, 117, 260, 108, 278, 387, 147, 245, 399, 227, 275, 1, 329,
411, 140, 341, 152, 55, 132, 17, 312, 168, 297, 92, 385, 345, 237,
119, 120, 102, 101, 44, 75, 94, 89, 332, 307, 74, 85, 212, 181,
118, 348, 23, 412, 413, 69, 196, 386, 235, 43, 383, 45, 210, 172,
286, 223, 256, 144, 183, 322, 253, 80, 261, 407, 291, 113, 301, 343,
353, 216, 115, 378, 134, 158, 86, 73, 404, 35, 66, 106, 251, 136,
161, 137, 274, 246, 8, 162, 97, 32, 157, 18, 91, 303, 410, 349,
290, 177, 53, 397, 111, 373, 127, 105, 28, 201, 84, 104, 125, 346,
323, 49, 347, 271, 304, 355, 208, 309, 124, 37, 344, 306, 360, 389,
31, 377, 392, 30, 39, 284, 163, 351, 10, 255, 395, 186, 382, 184,
82, 126, 130, 330, 142, 264, 342, 296, 123, 250, 20, 257, 313, 46,
268, 34, 289, 170, 308, 380, 150, 265, 371, 213, 93, 154, 333, 262,
61, 151, 232, 87, 56, 285, 206, 267, 263, 340, 217, 114, 317, 107,
63, 390, 121, 139, 6, 194, 225, 336, 242, 112, 320, 356, 248, 391,
283, 379, 29, 83, 64, 281, 292, 135, 42, 276, 324, 33, 402, 103,
204, 314, 156, 193, 364, 68, 244, 352, 110, 369, 187, 272, 214, 365,
252, 393, 359, 211, 164, 368, 400, 195, 40, 116, 279, 205, 259, 337,
319, 199, 7, 240, 77, 72, 370, 209, 145, 122, 207, 60, 405, 327,
160, 21, 273, 62, 334, 406, 100, 14, 197, 26, 311, 403, 16, 354,
129, 269, 71, 109, 315, 374, 203, 266, 220, 335, 175, 230, 302, 202,
19, 376, 153, 174, 328, 388, 188, 96, 138, 98, 287, 25, 326, 190,
191, 239, 288, 76, 50, 179, 299, 48, 3, 78, 11, 228, 280, 57,
215, 282, 2, 398, 149, 15, 47, 182, 300, 79, 236, 12, 249, 233,
131, 218, 362, 363, 58, 67, 357, 155, 0, 41, 24, 270, 293, 325,
229, 133, 9, 13, 277, 4, 298, 254, 65, 171, 358, 350, 180, 185,
316, 367, 221, 331, 143, 59, 258, 394, 165, 176, 401, 318, 189, 70,
27, 381, 231, 294, 141, 384, 95, 99], device='cuda:4')
sycamore n53 m14
180 31.057 3.008e+01 TimeUsed 50815
184 38.548 3.388e+01 TimeUsed 51936
188 38.586 3.213e+01 TimeUsed 53049
192 40.670 3.496e+01 TimeUsed 54152
| buffer.save_or_load_history(): Save ./task_TNCO_05/replay_buffer_states.pth torch.Size([165376, 484])
| buffer.save_or_load_history(): Save ./task_TNCO_05/replay_buffer_scores.pth torch.Size([165376, 1])
num_train: 165376
min_score: 16.610
avg_score: 31.641 ± 8.670
max_score: 55.691
best_result:
tensor([472, 25, 247, 61, 397, 262, 415, 217, 83, 444, 44, 439, 65, 325,
169, 277, 436, 178, 113, 337, 421, 90, 74, 120, 463, 107, 54, 85,
242, 140, 284, 50, 137, 268, 333, 331, 29, 57, 372, 168, 426, 468,
354, 19, 95, 462, 478, 228, 132, 37, 166, 417, 483, 28, 265, 78,
190, 11, 413, 56, 449, 139, 420, 310, 469, 423, 46, 311, 473, 435,
105, 264, 13, 10, 273, 252, 257, 390, 24, 23, 315, 279, 432, 470,
309, 70, 398, 158, 330, 117, 49, 127, 396, 111, 306, 165, 115, 451,
313, 477, 154, 5, 351, 112, 4, 18, 64, 334, 394, 293, 271, 185,
291, 123, 267, 285, 431, 230, 256, 94, 141, 76, 312, 314, 210, 101,
152, 191, 237, 365, 136, 440, 479, 12, 183, 251, 186, 419, 222, 48,
459, 130, 452, 355, 16, 453, 20, 188, 290, 209, 263, 92, 294, 434,
300, 344, 427, 322, 248, 89, 457, 77, 204, 181, 393, 177, 100, 464,
88, 246, 148, 27, 445, 225, 410, 134, 121, 475, 481, 382, 412, 348,
138, 170, 253, 245, 388, 467, 401, 131, 91, 80, 332, 395, 409, 180,
326, 53, 305, 455, 448, 298, 224, 1, 38, 159, 411, 189, 450, 36,
424, 200, 389, 167, 163, 150, 21, 93, 261, 270, 145, 308, 207, 366,
55, 383, 236, 255, 59, 233, 194, 377, 274, 405, 404, 289, 72, 342,
162, 384, 227, 428, 60, 283, 35, 122, 403, 379, 304, 22, 124, 97,
301, 26, 244, 349, 81, 429, 174, 171, 52, 418, 195, 460, 69, 218,
303, 387, 338, 425, 266, 297, 316, 125, 281, 128, 340, 280, 299, 149,
238, 184, 269, 205, 82, 482, 231, 116, 32, 386, 216, 360, 438, 58,
392, 110, 471, 282, 375, 229, 160, 381, 461, 43, 430, 443, 321, 402,
358, 276, 215, 118, 135, 370, 67, 129, 109, 433, 323, 339, 235, 208,
75, 42, 73, 239, 318, 320, 0, 41, 223, 153, 466, 68, 416, 206,
272, 442, 400, 378, 254, 84, 86, 437, 292, 221, 376, 199, 441, 341,
454, 119, 66, 104, 249, 34, 407, 259, 346, 9, 356, 307, 71, 17,
380, 335, 203, 374, 156, 361, 260, 147, 3, 106, 278, 202, 182, 258,
319, 143, 45, 363, 447, 275, 456, 324, 406, 47, 196, 7, 114, 474,
151, 179, 220, 31, 155, 133, 15, 175, 295, 329, 327, 362, 243, 192,
172, 476, 368, 328, 480, 144, 317, 353, 234, 161, 345, 213, 391, 212,
288, 232, 369, 164, 226, 87, 198, 446, 197, 63, 103, 6, 62, 187,
408, 33, 2, 352, 211, 108, 364, 51, 286, 142, 176, 465, 8, 350,
336, 458, 157, 214, 371, 399, 241, 193, 422, 302, 201, 414, 385, 357,
98, 96, 39, 79, 146, 126, 347, 102, 367, 250, 373, 343, 240, 99,
219, 40, 296, 173, 30, 287, 359, 14], device='cuda:5')
| buffer.save_or_load_history(): Save ./task_TNCO_01/replay_buffer_states.pth torch.Size([173568, 484])
| buffer.save_or_load_history(): Save ./task_TNCO_01/replay_buffer_scores.pth torch.Size([173568, 1])
num_train: 173568
min_score: 17.748
avg_score: 33.511 ± 7.540
max_score: 55.088
best_result:
tensor([ 62, 192, 176, 225, 412, 167, 55, 358, 44, 413, 223, 440, 387, 468,
315, 25, 275, 349, 309, 253, 75, 483, 273, 403, 443, 109, 448, 170,
416, 180, 245, 82, 110, 409, 76, 236, 59, 249, 480, 436, 124, 438,
472, 205, 133, 336, 479, 467, 404, 129, 41, 353, 343, 284, 475, 392,
421, 15, 281, 474, 73, 324, 127, 450, 267, 272, 179, 201, 365, 210,
202, 292, 466, 132, 368, 17, 263, 265, 199, 103, 391, 369, 381, 28,
137, 140, 89, 321, 340, 471, 323, 242, 34, 386, 300, 372, 370, 366,
117, 454, 226, 318, 338, 52, 191, 195, 98, 458, 168, 341, 294, 106,
235, 143, 86, 112, 282, 390, 243, 447, 411, 260, 111, 469, 394, 203,
35, 99, 431, 423, 0, 339, 432, 385, 94, 446, 74, 174, 382, 427,
335, 400, 279, 172, 121, 476, 445, 231, 128, 415, 308, 439, 2, 175,
345, 456, 342, 303, 364, 384, 306, 460, 452, 459, 157, 395, 389, 177,
61, 414, 22, 270, 244, 376, 104, 259, 173, 434, 429, 264, 331, 208,
189, 65, 453, 330, 407, 379, 332, 280, 250, 347, 317, 251, 402, 118,
478, 302, 257, 1, 266, 285, 136, 399, 38, 252, 45, 151, 451, 417,
360, 313, 293, 425, 482, 120, 49, 31, 477, 305, 54, 81, 367, 307,
47, 219, 152, 229, 420, 119, 378, 276, 182, 254, 256, 138, 422, 406,
261, 371, 319, 433, 57, 78, 134, 209, 334, 388, 304, 144, 437, 145,
283, 163, 135, 227, 424, 96, 455, 333, 9, 13, 48, 396, 158, 50,
204, 419, 87, 83, 314, 80, 147, 430, 237, 207, 269, 258, 310, 102,
380, 217, 435, 373, 463, 461, 286, 350, 14, 232, 326, 255, 290, 222,
383, 71, 320, 470, 46, 51, 405, 291, 344, 186, 105, 113, 361, 374,
214, 296, 18, 287, 441, 166, 114, 262, 21, 67, 398, 20, 162, 221,
442, 33, 329, 215, 77, 29, 278, 125, 246, 107, 218, 3, 7, 122,
240, 101, 481, 239, 53, 188, 426, 131, 464, 220, 297, 473, 213, 149,
377, 90, 130, 316, 354, 211, 288, 116, 159, 212, 449, 187, 91, 16,
56, 348, 274, 190, 100, 84, 36, 228, 24, 301, 37, 295, 6, 351,
79, 139, 63, 156, 462, 40, 418, 271, 97, 32, 43, 312, 363, 465,
178, 181, 154, 241, 153, 23, 19, 224, 12, 64, 298, 197, 95, 8,
26, 277, 357, 108, 184, 68, 428, 311, 327, 142, 233, 72, 123, 4,
410, 289, 238, 248, 299, 408, 58, 196, 148, 5, 194, 66, 401, 141,
60, 397, 444, 457, 216, 234, 10, 362, 356, 27, 126, 164, 328, 115,
322, 30, 169, 183, 171, 268, 337, 42, 92, 247, 185, 93, 206, 165,
161, 85, 355, 160, 346, 359, 325, 150, 88, 200, 69, 155, 146, 11,
393, 70, 352, 230, 39, 375, 193, 198], device='cuda:1')
sycamore n53 m16
164 27.696 2.399e+01 TimeUsed 49556
168 27.998 2.742e+01 TimeUsed 50687
172 28.348 2.523e+01 TimeUsed 51817
176 28.033 2.729e+01 TimeUsed 52947
| buffer.save_or_load_history(): Save ./task_TNCO_06/replay_buffer_states.pth torch.Size([153088, 585])
| buffer.save_or_load_history(): Save ./task_TNCO_06/replay_buffer_scores.pth torch.Size([153088, 1])
num_train: 153088
min_score: 22.014
avg_score: 32.901 ± 8.477
max_score: 66.528
best_result:
tensor([ 34, 111, 259, 102, 5, 546, 226, 129, 485, 194, 347, 115, 434, 480,
89, 8, 432, 100, 136, 288, 575, 24, 9, 84, 353, 117, 556, 348,
13, 240, 50, 142, 94, 415, 214, 383, 163, 337, 501, 137, 171, 544,
559, 525, 87, 73, 298, 384, 83, 296, 25, 35, 155, 520, 79, 93,
77, 449, 286, 334, 7, 220, 254, 518, 173, 37, 175, 253, 157, 248,
321, 38, 529, 169, 81, 300, 149, 72, 369, 176, 358, 68, 503, 16,
301, 304, 440, 45, 161, 164, 12, 103, 63, 506, 416, 104, 76, 108,
10, 66, 19, 165, 32, 486, 49, 195, 392, 121, 533, 473, 40, 48,
436, 507, 577, 126, 295, 492, 355, 297, 252, 18, 42, 477, 86, 562,
505, 15, 310, 56, 570, 275, 460, 78, 174, 314, 82, 185, 59, 251,
33, 67, 459, 409, 499, 130, 580, 187, 183, 114, 131, 203, 213, 210,
55, 456, 557, 410, 509, 6, 154, 44, 209, 170, 91, 29, 268, 179,
30, 62, 190, 22, 168, 258, 274, 85, 330, 151, 308, 116, 225, 217,
39, 106, 345, 46, 469, 140, 202, 234, 419, 36, 521, 579, 201, 20,
435, 290, 223, 92, 272, 364, 58, 23, 303, 294, 189, 124, 576, 244,
282, 316, 429, 583, 237, 166, 371, 255, 153, 186, 156, 243, 522, 306,
27, 167, 508, 158, 177, 1, 249, 452, 277, 534, 178, 470, 401, 229,
95, 379, 426, 3, 211, 540, 262, 327, 250, 447, 284, 430, 74, 199,
263, 230, 221, 90, 53, 198, 487, 389, 457, 519, 132, 57, 61, 107,
476, 417, 88, 438, 498, 312, 224, 340, 28, 196, 118, 554, 569, 390,
241, 152, 465, 188, 502, 11, 242, 537, 299, 451, 260, 97, 289, 500,
105, 135, 515, 208, 41, 311, 245, 582, 69, 479, 464, 424, 99, 150,
109, 514, 52, 404, 346, 560, 418, 336, 101, 302, 467, 4, 542, 328,
391, 26, 207, 96, 402, 191, 405, 51, 394, 581, 439, 536, 511, 494,
535, 568, 239, 17, 322, 551, 31, 80, 138, 122, 552, 112, 555, 162,
276, 145, 228, 359, 2, 280, 414, 285, 516, 442, 566, 139, 474, 119,
482, 382, 386, 238, 206, 54, 512, 431, 212, 216, 375, 335, 446, 70,
397, 193, 549, 293, 60, 541, 292, 219, 481, 584, 472, 147, 43, 325,
388, 413, 172, 133, 381, 463, 543, 146, 396, 160, 200, 437, 399, 338,
271, 278, 222, 315, 313, 144, 455, 215, 326, 571, 125, 123, 287, 517,
497, 71, 184, 548, 233, 256, 247, 339, 393, 530, 197, 235, 361, 291,
269, 550, 344, 567, 134, 370, 513, 532, 496, 351, 283, 281, 428, 333,
433, 407, 564, 331, 458, 504, 180, 342, 143, 400, 218, 323, 547, 526,
350, 265, 110, 488, 64, 478, 398, 454, 420, 489, 231, 246, 354, 362,
329, 450, 368, 468, 376, 538, 483, 493, 21, 412, 373, 528, 318, 510,
423, 471, 356, 453, 377, 181, 319, 352, 523, 411, 267, 374, 578, 563,
425, 558, 363, 367, 406, 490, 484, 141, 422, 279, 320, 257, 357, 462,
531, 527, 0, 204, 273, 475, 261, 408, 192, 495, 305, 47, 574, 443,
113, 444, 309, 236, 378, 466, 448, 205, 128, 232, 573, 545, 227, 264,
14, 445, 98, 365, 343, 270, 403, 349, 395, 159, 341, 266, 317, 385,
553, 324, 539, 387, 332, 427, 441, 380, 561, 461, 127, 366, 524, 491,
182, 360, 148, 307, 65, 565, 372, 120, 75, 572, 421],
device='cuda:6')
148 41.338 4.493e+01 TimeUsed 51655
152 41.338 4.496e+01 TimeUsed 52946
156 41.338 4.490e+01 TimeUsed 54235
160 41.338 4.495e+01 TimeUsed 55519
| buffer.save_or_load_history(): Save ./task_TNCO_02/replay_buffer_states.pth torch.Size([124416, 585])
| buffer.save_or_load_history(): Save ./task_TNCO_02/replay_buffer_scores.pth torch.Size([124416, 1])
num_train: 124416
min_score: 32.511
avg_score: 45.043 ± 3.887
max_score: 63.216
best_result:
tensor([272, 432, 175, 301, 92, 35, 338, 430, 482, 223, 57, 298, 544, 524,
89, 277, 147, 63, 96, 526, 53, 459, 166, 484, 16, 78, 105, 391,
258, 59, 214, 344, 374, 260, 576, 573, 230, 163, 393, 394, 247, 503,
61, 186, 559, 468, 84, 375, 512, 43, 499, 100, 434, 396, 362, 115,
322, 328, 521, 197, 90, 69, 557, 456, 30, 469, 245, 324, 577, 201,
404, 407, 358, 178, 356, 240, 10, 498, 192, 364, 545, 487, 87, 465,
141, 269, 540, 529, 98, 299, 122, 562, 11, 226, 509, 537, 56, 547,
159, 120, 363, 383, 44, 158, 399, 496, 17, 392, 238, 118, 21, 458,
135, 477, 292, 339, 382, 486, 131, 489, 209, 551, 6, 132, 74, 443,
148, 203, 29, 333, 531, 107, 142, 143, 232, 287, 329, 108, 334, 471,
130, 532, 208, 241, 450, 513, 207, 307, 311, 104, 371, 257, 161, 67,
229, 470, 7, 481, 190, 234, 410, 134, 253, 12, 502, 51, 368, 366,
151, 273, 316, 560, 571, 376, 504, 478, 289, 427, 262, 31, 568, 294,
473, 81, 506, 97, 536, 45, 83, 348, 167, 525, 215, 409, 405, 13,
523, 553, 138, 291, 565, 424, 109, 541, 255, 28, 555, 187, 491, 429,
189, 220, 94, 288, 24, 516, 554, 490, 530, 421, 466, 351, 99, 580,
58, 435, 442, 461, 200, 389, 310, 155, 497, 480, 317, 42, 26, 55,
542, 25, 37, 390, 39, 65, 515, 173, 110, 402, 128, 121, 267, 236,
448, 176, 422, 567, 341, 403, 462, 250, 5, 268, 218, 517, 14, 263,
412, 373, 231, 282, 385, 41, 204, 248, 500, 146, 103, 418, 538, 210,
271, 274, 377, 290, 79, 438, 347, 534, 549, 352, 244, 9, 22, 264,
93, 284, 34, 372, 441, 32, 357, 225, 350, 233, 460, 68, 297, 179,
395, 314, 444, 171, 2, 194, 119, 400, 401, 417, 36, 451, 343, 242,
464, 453, 527, 423, 124, 283, 408, 219, 494, 19, 227, 514, 202, 275,
381, 455, 505, 420, 20, 413, 75, 380, 137, 397, 359, 126, 495, 205,
349, 123, 337, 312, 106, 566, 419, 340, 361, 533, 369, 50, 387, 452,
70, 346, 406, 145, 164, 222, 501, 326, 156, 384, 86, 518, 116, 180,
508, 546, 72, 153, 212, 243, 426, 335, 184, 216, 575, 556, 353, 446,
327, 181, 54, 305, 80, 398, 71, 574, 144, 416, 439, 188, 437, 48,
150, 296, 309, 52, 60, 237, 325, 206, 162, 252, 433, 251, 49, 196,
428, 195, 476, 217, 111, 18, 112, 261, 169, 415, 133, 519, 259, 522,
127, 152, 386, 76, 475, 510, 62, 457, 102, 535, 454, 3, 300, 431,
4, 185, 388, 479, 315, 246, 125, 15, 285, 488, 293, 308, 213, 539,
507, 286, 276, 511, 280, 91, 129, 85, 191, 140, 414, 88, 73, 569,
550, 154, 306, 198, 483, 168, 447, 436, 279, 548, 320, 331, 228, 174,
170, 463, 221, 360, 183, 0, 467, 40, 66, 27, 165, 224, 266, 342,
95, 543, 440, 304, 583, 572, 321, 64, 303, 1, 570, 492, 579, 235,
149, 367, 330, 113, 77, 47, 578, 254, 313, 193, 411, 449, 365, 160,
552, 355, 82, 564, 199, 33, 256, 319, 425, 114, 332, 23, 139, 117,
295, 354, 270, 472, 46, 528, 265, 101, 379, 558, 182, 157, 323, 581,
249, 177, 8, 561, 211, 584, 378, 336, 318, 563, 370, 38, 445, 278,
239, 172, 520, 474, 302, 281, 345, 493, 582, 136, 485],
device='cuda:2')
sycamore n53 m20
132 51.615 5.084e+01 TimeUsed 45091
136 60.517 5.007e+01 TimeUsed 46474
140 55.095 5.104e+01 TimeUsed 47911
144 49.479 4.995e+01 TimeUsed 49360
| buffer.save_or_load_history(): Save ./task_TNCO_07/replay_buffer_states.pth torch.Size([128512, 754])
| buffer.save_or_load_history(): Save ./task_TNCO_07/replay_buffer_scores.pth torch.Size([128512, 1])
num_train: 128512
min_score: 21.782
avg_score: 42.245 ± 14.739
max_score: 81.880
best_result:
tensor([ 7, 472, 540, 176, 184, 170, 16, 649, 433, 294, 20, 536, 159, 732,
239, 233, 513, 616, 655, 665, 696, 116, 70, 30, 382, 52, 362, 293,
272, 497, 99, 288, 36, 134, 600, 548, 594, 21, 157, 331, 624, 520,
558, 85, 81, 35, 752, 123, 286, 658, 417, 40, 353, 583, 264, 675,
436, 736, 61, 753, 494, 202, 747, 140, 22, 461, 717, 330, 617, 237,
670, 8, 644, 674, 599, 221, 78, 462, 457, 621, 348, 87, 240, 299,
88, 152, 114, 195, 13, 634, 618, 700, 210, 663, 47, 751, 241, 652,
408, 137, 103, 302, 329, 542, 62, 95, 724, 55, 470, 175, 439, 585,
366, 53, 54, 297, 500, 280, 373, 476, 650, 706, 393, 58, 39, 105,
147, 591, 6, 703, 282, 409, 667, 686, 489, 29, 250, 110, 466, 231,
209, 654, 259, 11, 93, 361, 121, 146, 746, 407, 63, 501, 639, 350,
458, 718, 452, 672, 314, 72, 482, 42, 571, 17, 19, 699, 128, 248,
316, 298, 283, 573, 588, 177, 648, 593, 543, 132, 739, 122, 442, 130,
199, 702, 212, 92, 357, 628, 263, 343, 50, 113, 290, 664, 691, 69,
136, 406, 380, 309, 43, 631, 481, 115, 638, 51, 189, 414, 666, 144,
281, 312, 656, 450, 173, 518, 449, 623, 172, 255, 245, 491, 635, 161,
325, 226, 10, 561, 557, 590, 659, 56, 71, 563, 97, 580, 677, 689,
26, 605, 229, 200, 64, 394, 308, 211, 311, 473, 160, 76, 695, 426,
187, 106, 196, 586, 80, 225, 632, 749, 443, 253, 48, 5, 24, 460,
155, 535, 640, 14, 169, 28, 574, 611, 90, 307, 102, 614, 636, 224,
98, 399, 714, 630, 722, 685, 688, 733, 744, 531, 582, 234, 377, 295,
701, 397, 587, 419, 738, 488, 727, 748, 83, 218, 653, 487, 745, 499,
673, 669, 301, 525, 519, 220, 391, 188, 705, 533, 579, 287, 423, 205,
277, 117, 538, 602, 597, 0, 246, 60, 306, 32, 179, 186, 104, 336,
742, 164, 564, 151, 740, 680, 284, 615, 413, 107, 126, 465, 296, 45,
704, 683, 731, 338, 34, 396, 206, 710, 687, 507, 534, 201, 1, 578,
529, 73, 238, 646, 84, 344, 185, 3, 569, 455, 651, 257, 235, 537,
467, 360, 438, 204, 149, 244, 562, 613, 125, 468, 448, 459, 182, 135,
89, 498, 174, 347, 165, 641, 12, 425, 349, 625, 265, 642, 303, 693,
715, 216, 322, 608, 729, 269, 698, 719, 351, 671, 647, 446, 523, 428,
345, 508, 629, 566, 403, 496, 289, 405, 668, 150, 440, 124, 643, 555,
575, 31, 112, 620, 274, 682, 552, 390, 215, 511, 681, 305, 469, 383,
598, 463, 708, 94, 207, 256, 424, 15, 577, 725, 378, 434, 193, 387,
291, 120, 219, 502, 214, 372, 324, 544, 432, 57, 661, 119, 127, 431,
493, 9, 633, 713, 388, 168, 365, 75, 512, 415, 367, 46, 730, 480,
252, 363, 332, 514, 368, 505, 441, 258, 131, 679, 96, 567, 154, 59,
516, 249, 340, 697, 213, 352, 418, 273, 356, 384, 435, 261, 33, 260,
741, 223, 622, 66, 515, 167, 38, 129, 547, 158, 524, 743, 18, 232,
707, 82, 320, 278, 141, 262, 416, 607, 191, 503, 203, 589, 437, 581,
190, 554, 549, 402, 358, 317, 242, 208, 180, 601, 333, 726, 381, 148,
197, 592, 471, 156, 556, 392, 326, 153, 522, 268, 572, 657, 477, 716,
545, 398, 750, 389, 77, 371, 300, 27, 596, 101, 610, 532, 485, 712,
690, 143, 342, 411, 270, 464, 341, 728, 490, 44, 109, 227, 404, 711,
456, 474, 737, 709, 254, 236, 721, 521, 546, 527, 395, 385, 276, 285,
65, 484, 412, 346, 163, 570, 660, 319, 162, 483, 327, 504, 138, 429,
67, 445, 645, 420, 506, 479, 68, 478, 375, 410, 145, 108, 49, 228,
453, 335, 530, 517, 553, 626, 133, 510, 142, 568, 74, 694, 528, 91,
279, 619, 684, 334, 217, 495, 576, 386, 181, 376, 271, 23, 627, 41,
541, 194, 267, 318, 447, 444, 734, 509, 637, 337, 310, 339, 379, 171,
251, 292, 198, 400, 321, 735, 328, 354, 374, 662, 118, 430, 475, 192,
539, 178, 247, 427, 166, 139, 565, 364, 492, 584, 25, 526, 692, 720,
401, 486, 230, 2, 4, 606, 595, 275, 678, 37, 100, 676, 266, 723,
304, 604, 422, 355, 451, 551, 359, 559, 609, 79, 369, 612, 183, 222,
550, 323, 370, 560, 603, 86, 315, 111, 313, 421, 454, 243],
device='cuda:7')
180 24.698 2.255e+01 TimeUsed 54635
184 23.457 2.302e+01 TimeUsed 55779
188 23.251 2.314e+01 TimeUsed 56921
192 23.210 2.244e+01 TimeUsed 58058
| buffer.save_or_load_history(): Save ./task_TNCO_03/replay_buffer_states.pth torch.Size([148992, 754])
| buffer.save_or_load_history(): Save ./task_TNCO_03/replay_buffer_scores.pth torch.Size([148992, 1])
num_train: 148992
min_score: 22.585
avg_score: 29.913 ± 6.161
max_score: 74.354
best_result:
tensor([174, 109, 696, 219, 728, 499, 271, 25, 198, 661, 273, 371, 639, 709,
587, 704, 63, 386, 65, 529, 721, 606, 677, 274, 657, 693, 135, 479,
619, 257, 141, 36, 665, 221, 613, 368, 189, 396, 656, 534, 706, 726,
622, 452, 735, 710, 659, 634, 336, 694, 275, 747, 76, 592, 100, 545,
685, 687, 537, 580, 616, 6, 31, 701, 247, 217, 101, 690, 82, 89,
635, 543, 435, 598, 720, 733, 47, 289, 484, 723, 129, 199, 708, 680,
234, 267, 551, 427, 583, 751, 476, 638, 251, 448, 290, 642, 472, 169,
461, 303, 363, 610, 575, 34, 670, 17, 550, 489, 729, 238, 655, 526,
308, 212, 516, 381, 513, 459, 48, 117, 699, 487, 581, 15, 650, 707,
446, 549, 584, 514, 542, 132, 666, 664, 679, 481, 152, 12, 668, 722,
528, 507, 160, 540, 127, 566, 719, 328, 498, 495, 58, 623, 387, 122,
500, 740, 466, 496, 743, 682, 501, 102, 269, 434, 567, 350, 225, 739,
457, 648, 126, 594, 378, 333, 20, 654, 55, 480, 404, 27, 462, 59,
643, 177, 520, 698, 423, 230, 231, 653, 471, 574, 713, 260, 585, 107,
737, 261, 125, 742, 475, 552, 155, 366, 605, 437, 18, 397, 439, 689,
440, 676, 445, 450, 558, 614, 620, 324, 724, 546, 154, 424, 458, 315,
521, 658, 678, 2, 660, 384, 342, 425, 186, 74, 474, 104, 531, 732,
241, 403, 727, 544, 478, 151, 712, 253, 607, 673, 460, 688, 150, 506,
612, 71, 357, 414, 595, 103, 662, 56, 184, 441, 220, 512, 647, 505,
391, 530, 252, 358, 222, 700, 465, 429, 292, 173, 139, 149, 752, 625,
319, 519, 536, 442, 485, 493, 675, 268, 603, 123, 411, 301, 143, 556,
398, 633, 79, 374, 23, 624, 503, 266, 525, 232, 277, 158, 118, 428,
121, 110, 340, 373, 589, 553, 280, 278, 144, 377, 146, 114, 159, 54,
380, 78, 62, 563, 343, 108, 432, 686, 361, 214, 750, 156, 283, 572,
579, 32, 630, 182, 286, 346, 646, 590, 53, 482, 745, 577, 467, 120,
112, 554, 671, 578, 235, 51, 248, 748, 692, 591, 35, 210, 88, 749,
92, 67, 330, 296, 299, 14, 4, 443, 52, 229, 179, 548, 753, 444,
138, 628, 262, 611, 228, 180, 325, 399, 718, 645, 497, 197, 137, 504,
420, 300, 409, 352, 178, 3, 621, 245, 438, 491, 532, 364, 22, 205,
284, 293, 629, 407, 326, 96, 741, 691, 44, 270, 641, 559, 515, 695,
233, 236, 307, 569, 702, 672, 632, 744, 111, 711, 187, 46, 145, 667,
637, 421, 181, 317, 116, 164, 483, 683, 239, 98, 320, 168, 356, 33,
573, 39, 412, 115, 49, 402, 738, 599, 130, 298, 570, 28, 568, 608,
60, 674, 140, 26, 533, 50, 736, 40, 627, 162, 517, 80, 463, 91,
418, 631, 68, 72, 451, 341, 615, 564, 57, 172, 408, 431, 90, 309,
714, 557, 43, 502, 602, 285, 249, 243, 617, 447, 183, 494, 716, 597,
287, 510, 640, 196, 522, 347, 64, 97, 362, 383, 83, 304, 405, 468,
147, 413, 348, 191, 426, 360, 95, 555, 524, 29, 565, 337, 681, 600,
226, 527, 16, 410, 746, 216, 453, 11, 393, 389, 5, 106, 588, 394,
596, 684, 539, 433, 382, 312, 10, 354, 314, 535, 470, 369, 244, 282,
255, 417, 87, 94, 626, 105, 201, 69, 188, 652, 24, 593, 669, 209,
547, 264, 200, 379, 166, 311, 223, 609, 338, 19, 165, 37, 204, 335,
492, 207, 203, 250, 636, 464, 276, 355, 73, 456, 224, 77, 119, 390,
725, 0, 351, 124, 509, 42, 436, 601, 175, 734, 246, 586, 192, 302,
508, 649, 430, 703, 194, 41, 185, 511, 316, 157, 240, 66, 469, 281,
604, 353, 8, 288, 345, 218, 136, 45, 717, 75, 171, 242, 237, 488,
477, 81, 86, 651, 518, 99, 30, 571, 61, 9, 705, 259, 322, 375,
359, 265, 134, 490, 331, 254, 161, 193, 305, 419, 195, 385, 7, 327,
318, 560, 93, 372, 306, 392, 538, 211, 395, 167, 21, 370, 70, 344,
334, 422, 202, 576, 339, 170, 310, 523, 697, 85, 473, 215, 227, 618,
148, 163, 291, 449, 279, 313, 455, 176, 142, 256, 365, 561, 367, 131,
663, 258, 13, 329, 213, 486, 323, 582, 415, 541, 38, 113, 133, 321,
731, 128, 644, 376, 295, 349, 208, 401, 84, 715, 332, 416, 206, 406,
730, 297, 454, 1, 190, 153, 272, 263, 294, 400, 562, 388],
device='cuda:3')