File size: 43,560 Bytes
1c7d5fa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 |
[2023-09-05 10:58:35,050][272918] Saving configuration to /media/ml1/data/nogletrading/ppo_vizdoom/train_dir/default_experiment/config.json... [2023-09-05 10:58:35,052][272918] Rollout worker 0 uses device cpu [2023-09-05 10:58:35,052][272918] Rollout worker 1 uses device cpu [2023-09-05 10:58:35,052][272918] Rollout worker 2 uses device cpu [2023-09-05 10:58:35,052][272918] Rollout worker 3 uses device cpu [2023-09-05 10:58:35,053][272918] Rollout worker 4 uses device cpu [2023-09-05 10:58:35,053][272918] Rollout worker 5 uses device cpu [2023-09-05 10:58:35,053][272918] Rollout worker 6 uses device cpu [2023-09-05 10:58:35,054][272918] Rollout worker 7 uses device cpu [2023-09-05 10:58:35,130][272918] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2023-09-05 10:58:35,130][272918] InferenceWorker_p0-w0: min num requests: 2 [2023-09-05 10:58:35,163][272918] Starting all processes... [2023-09-05 10:58:35,163][272918] Starting process learner_proc0 [2023-09-05 10:58:37,092][272918] Starting all processes... [2023-09-05 10:58:37,106][272918] Starting process inference_proc0-0 [2023-09-05 10:58:37,107][272918] Starting process rollout_proc0 [2023-09-05 10:58:37,108][273075] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2023-09-05 10:58:37,109][273075] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0 [2023-09-05 10:58:37,107][272918] Starting process rollout_proc1 [2023-09-05 10:58:37,108][272918] Starting process rollout_proc2 [2023-09-05 10:58:37,111][272918] Starting process rollout_proc3 [2023-09-05 10:58:37,118][273075] Num visible devices: 1 [2023-09-05 10:58:37,111][272918] Starting process rollout_proc4 [2023-09-05 10:58:37,112][272918] Starting process rollout_proc5 [2023-09-05 10:58:37,114][272918] Starting process rollout_proc6 [2023-09-05 10:58:37,114][272918] Starting process rollout_proc7 [2023-09-05 10:58:37,198][273075] Starting seed is not provided [2023-09-05 10:58:37,199][273075] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2023-09-05 10:58:37,200][273075] Initializing actor-critic model on device cuda:0 [2023-09-05 10:58:37,201][273075] RunningMeanStd input shape: (3, 72, 128) [2023-09-05 10:58:37,204][273075] RunningMeanStd input shape: (1,) [2023-09-05 10:58:37,245][273075] ConvEncoder: input_channels=3 [2023-09-05 10:58:37,545][273075] Conv encoder output size: 512 [2023-09-05 10:58:37,546][273075] Policy head output size: 512 [2023-09-05 10:58:37,568][273075] Created Actor Critic model with architecture: [2023-09-05 10:58:37,568][273075] ActorCriticSharedWeights( (obs_normalizer): ObservationNormalizer( (running_mean_std): RunningMeanStdDictInPlace( (running_mean_std): ModuleDict( (obs): RunningMeanStdInPlace() ) ) ) (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace) (encoder): VizdoomEncoder( (basic_encoder): ConvEncoder( (enc): RecursiveScriptModule( original_name=ConvEncoderImpl (conv_head): RecursiveScriptModule( original_name=Sequential (0): RecursiveScriptModule(original_name=Conv2d) (1): RecursiveScriptModule(original_name=ELU) (2): RecursiveScriptModule(original_name=Conv2d) (3): RecursiveScriptModule(original_name=ELU) (4): RecursiveScriptModule(original_name=Conv2d) (5): RecursiveScriptModule(original_name=ELU) ) (mlp_layers): RecursiveScriptModule( original_name=Sequential (0): RecursiveScriptModule(original_name=Linear) (1): RecursiveScriptModule(original_name=ELU) ) ) ) ) (core): ModelCoreRNN( (core): GRU(512, 512) ) (decoder): MlpDecoder( (mlp): Identity() ) (critic_linear): Linear(in_features=512, out_features=1, bias=True) (action_parameterization): ActionParameterizationDefault( (distribution_linear): Linear(in_features=512, out_features=5, bias=True) ) ) [2023-09-05 10:58:40,269][273148] Worker 2 uses CPU cores [2] [2023-09-05 10:58:40,288][273075] Using optimizer <class 'torch.optim.adam.Adam'> [2023-09-05 10:58:40,291][273075] No checkpoints found [2023-09-05 10:58:40,292][273075] Did not load from checkpoint, starting from scratch! [2023-09-05 10:58:40,293][273075] Initialized policy 0 weights for model version 0 [2023-09-05 10:58:40,301][273075] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2023-09-05 10:58:40,325][273075] LearnerWorker_p0 finished initialization! [2023-09-05 10:58:40,825][273157] Worker 4 uses CPU cores [4] [2023-09-05 10:58:41,270][273146] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2023-09-05 10:58:41,271][273146] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0 [2023-09-05 10:58:41,280][273146] Num visible devices: 1 [2023-09-05 10:58:41,504][273146] RunningMeanStd input shape: (3, 72, 128) [2023-09-05 10:58:41,507][273146] RunningMeanStd input shape: (1,) [2023-09-05 10:58:41,573][273146] ConvEncoder: input_channels=3 [2023-09-05 10:58:41,784][273146] Conv encoder output size: 512 [2023-09-05 10:58:41,786][273146] Policy head output size: 512 [2023-09-05 10:58:41,847][273147] Worker 0 uses CPU cores [0] [2023-09-05 10:58:42,376][273149] Worker 1 uses CPU cores [1] [2023-09-05 10:58:42,765][273165] Worker 6 uses CPU cores [6] [2023-09-05 10:58:43,101][273160] Worker 7 uses CPU cores [7] [2023-09-05 10:58:43,329][273162] Worker 3 uses CPU cores [3] [2023-09-05 10:58:43,474][272918] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 0. Throughput: 0: nan. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) [2023-09-05 10:58:43,484][273164] Worker 5 uses CPU cores [5] [2023-09-05 10:58:44,287][272918] Inference worker 0-0 is ready! [2023-09-05 10:58:44,287][272918] All inference workers are ready! Signal rollout workers to start! [2023-09-05 10:58:44,335][272918] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 0.0. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) [2023-09-05 10:58:44,366][273160] Doom resolution: 160x120, resize resolution: (128, 72) [2023-09-05 10:58:44,367][273148] Doom resolution: 160x120, resize resolution: (128, 72) [2023-09-05 10:58:44,370][273147] Doom resolution: 160x120, resize resolution: (128, 72) [2023-09-05 10:58:44,376][273149] Doom resolution: 160x120, resize resolution: (128, 72) [2023-09-05 10:58:44,386][273165] Doom resolution: 160x120, resize resolution: (128, 72) [2023-09-05 10:58:44,389][273162] Doom resolution: 160x120, resize resolution: (128, 72) [2023-09-05 10:58:44,392][273164] Doom resolution: 160x120, resize resolution: (128, 72) [2023-09-05 10:58:44,409][273157] Doom resolution: 160x120, resize resolution: (128, 72) [2023-09-05 10:58:44,936][273148] Decorrelating experience for 0 frames... [2023-09-05 10:58:44,936][273147] Decorrelating experience for 0 frames... [2023-09-05 10:58:44,939][273164] Decorrelating experience for 0 frames... [2023-09-05 10:58:44,939][273160] Decorrelating experience for 0 frames... [2023-09-05 10:58:44,941][273149] Decorrelating experience for 0 frames... [2023-09-05 10:58:44,941][273162] Decorrelating experience for 0 frames... [2023-09-05 10:58:45,312][273162] Decorrelating experience for 32 frames... [2023-09-05 10:58:45,313][273147] Decorrelating experience for 32 frames... [2023-09-05 10:58:45,314][273148] Decorrelating experience for 32 frames... [2023-09-05 10:58:45,320][273164] Decorrelating experience for 32 frames... [2023-09-05 10:58:45,325][273149] Decorrelating experience for 32 frames... [2023-09-05 10:58:45,375][273165] Decorrelating experience for 0 frames... [2023-09-05 10:58:45,388][273157] Decorrelating experience for 0 frames... [2023-09-05 10:58:45,644][273160] Decorrelating experience for 32 frames... [2023-09-05 10:58:45,731][273147] Decorrelating experience for 64 frames... [2023-09-05 10:58:45,745][273164] Decorrelating experience for 64 frames... [2023-09-05 10:58:45,754][273165] Decorrelating experience for 32 frames... [2023-09-05 10:58:45,838][273148] Decorrelating experience for 64 frames... [2023-09-05 10:58:46,055][273147] Decorrelating experience for 96 frames... [2023-09-05 10:58:46,072][273160] Decorrelating experience for 64 frames... [2023-09-05 10:58:46,156][273162] Decorrelating experience for 64 frames... [2023-09-05 10:58:46,164][273149] Decorrelating experience for 64 frames... [2023-09-05 10:58:46,167][273164] Decorrelating experience for 96 frames... [2023-09-05 10:58:46,202][273165] Decorrelating experience for 64 frames... [2023-09-05 10:58:46,485][273160] Decorrelating experience for 96 frames... [2023-09-05 10:58:46,537][273148] Decorrelating experience for 96 frames... [2023-09-05 10:58:46,585][273157] Decorrelating experience for 32 frames... [2023-09-05 10:58:46,609][273149] Decorrelating experience for 96 frames... [2023-09-05 10:58:46,636][273162] Decorrelating experience for 96 frames... [2023-09-05 10:58:46,821][273165] Decorrelating experience for 96 frames... [2023-09-05 10:58:47,141][273157] Decorrelating experience for 64 frames... [2023-09-05 10:58:47,523][273157] Decorrelating experience for 96 frames... [2023-09-05 10:58:47,915][273075] Signal inference workers to stop experience collection... [2023-09-05 10:58:47,920][273146] InferenceWorker_p0-w0: stopping experience collection [2023-09-05 10:58:48,900][273075] Signal inference workers to resume experience collection... [2023-09-05 10:58:48,901][273146] InferenceWorker_p0-w0: resuming experience collection [2023-09-05 10:58:49,335][272918] Fps is (10 sec: 698.8, 60 sec: 698.8, 300 sec: 698.8). Total num frames: 4096. Throughput: 0: 174.0. Samples: 1020. Policy #0 lag: (min: 0.0, avg: 0.0, max: 0.0) [2023-09-05 10:58:49,335][272918] Avg episode reward: [(0, '2.753')] [2023-09-05 10:58:51,929][273146] Updated weights for policy 0, policy_version 10 (0.0297) [2023-09-05 10:58:54,335][272918] Fps is (10 sec: 6963.3, 60 sec: 6411.3, 300 sec: 6411.3). Total num frames: 69632. Throughput: 0: 1441.0. Samples: 15650. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2023-09-05 10:58:54,335][272918] Avg episode reward: [(0, '4.479')] [2023-09-05 10:58:54,955][273146] Updated weights for policy 0, policy_version 20 (0.0021) [2023-09-05 10:58:55,121][272918] Heartbeat connected on Batcher_0 [2023-09-05 10:58:55,125][272918] Heartbeat connected on LearnerWorker_p0 [2023-09-05 10:58:55,136][272918] Heartbeat connected on RolloutWorker_w0 [2023-09-05 10:58:55,137][272918] Heartbeat connected on InferenceWorker_p0-w0 [2023-09-05 10:58:55,140][272918] Heartbeat connected on RolloutWorker_w1 [2023-09-05 10:58:55,142][272918] Heartbeat connected on RolloutWorker_w2 [2023-09-05 10:58:55,149][272918] Heartbeat connected on RolloutWorker_w3 [2023-09-05 10:58:55,156][272918] Heartbeat connected on RolloutWorker_w5 [2023-09-05 10:58:55,157][272918] Heartbeat connected on RolloutWorker_w6 [2023-09-05 10:58:55,169][272918] Heartbeat connected on RolloutWorker_w7 [2023-09-05 10:58:55,177][272918] Heartbeat connected on RolloutWorker_w4 [2023-09-05 10:58:57,912][273146] Updated weights for policy 0, policy_version 30 (0.0023) [2023-09-05 10:58:59,335][272918] Fps is (10 sec: 13516.9, 60 sec: 8780.3, 300 sec: 8780.3). Total num frames: 139264. Throughput: 0: 2262.8. Samples: 35890. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2023-09-05 10:58:59,335][272918] Avg episode reward: [(0, '4.458')] [2023-09-05 10:58:59,341][273075] Saving new best policy, reward=4.458! [2023-09-05 10:59:01,040][273146] Updated weights for policy 0, policy_version 40 (0.0024) [2023-09-05 10:59:04,120][273146] Updated weights for policy 0, policy_version 50 (0.0023) [2023-09-05 10:59:04,334][272918] Fps is (10 sec: 13516.9, 60 sec: 9817.5, 300 sec: 9817.5). Total num frames: 204800. Throughput: 0: 2193.2. Samples: 45752. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-09-05 10:59:04,335][272918] Avg episode reward: [(0, '4.259')] [2023-09-05 10:59:07,182][273146] Updated weights for policy 0, policy_version 60 (0.0022) [2023-09-05 10:59:09,335][272918] Fps is (10 sec: 13516.9, 60 sec: 10611.8, 300 sec: 10611.8). Total num frames: 274432. Throughput: 0: 2555.9. Samples: 66098. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-09-05 10:59:09,335][272918] Avg episode reward: [(0, '4.337')] [2023-09-05 10:59:10,137][273146] Updated weights for policy 0, policy_version 70 (0.0026) [2023-09-05 10:59:13,193][273146] Updated weights for policy 0, policy_version 80 (0.0021) [2023-09-05 10:59:14,335][272918] Fps is (10 sec: 13516.7, 60 sec: 11016.2, 300 sec: 11016.2). Total num frames: 339968. Throughput: 0: 2790.7. Samples: 86124. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2023-09-05 10:59:14,335][272918] Avg episode reward: [(0, '4.386')] [2023-09-05 10:59:16,324][273146] Updated weights for policy 0, policy_version 90 (0.0021) [2023-09-05 10:59:19,335][272918] Fps is (10 sec: 13107.2, 60 sec: 11307.7, 300 sec: 11307.7). Total num frames: 405504. Throughput: 0: 2681.4. Samples: 96156. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 10:59:19,335][272918] Avg episode reward: [(0, '4.795')] [2023-09-05 10:59:19,341][273075] Saving new best policy, reward=4.795! [2023-09-05 10:59:19,499][273146] Updated weights for policy 0, policy_version 100 (0.0019) [2023-09-05 10:59:22,787][273146] Updated weights for policy 0, policy_version 110 (0.0022) [2023-09-05 10:59:24,335][272918] Fps is (10 sec: 12697.4, 60 sec: 11427.6, 300 sec: 11427.6). Total num frames: 466944. Throughput: 0: 2810.7. Samples: 114848. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 10:59:24,337][272918] Avg episode reward: [(0, '4.540')] [2023-09-05 10:59:26,017][273146] Updated weights for policy 0, policy_version 120 (0.0023) [2023-09-05 10:59:29,065][273146] Updated weights for policy 0, policy_version 130 (0.0021) [2023-09-05 10:59:29,335][272918] Fps is (10 sec: 12697.5, 60 sec: 11610.7, 300 sec: 11610.7). Total num frames: 532480. Throughput: 0: 2983.6. Samples: 134262. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) [2023-09-05 10:59:29,336][272918] Avg episode reward: [(0, '4.593')] [2023-09-05 10:59:32,211][273146] Updated weights for policy 0, policy_version 140 (0.0023) [2023-09-05 10:59:34,335][272918] Fps is (10 sec: 13107.4, 60 sec: 11757.9, 300 sec: 11757.9). Total num frames: 598016. Throughput: 0: 3181.7. Samples: 144196. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 10:59:34,335][272918] Avg episode reward: [(0, '4.554')] [2023-09-05 10:59:35,265][273146] Updated weights for policy 0, policy_version 150 (0.0025) [2023-09-05 10:59:38,077][273146] Updated weights for policy 0, policy_version 160 (0.0022) [2023-09-05 10:59:39,335][272918] Fps is (10 sec: 13516.9, 60 sec: 11952.0, 300 sec: 11952.0). Total num frames: 667648. Throughput: 0: 3317.8. Samples: 164952. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2023-09-05 10:59:39,335][272918] Avg episode reward: [(0, '4.551')] [2023-09-05 10:59:41,172][273146] Updated weights for policy 0, policy_version 170 (0.0023) [2023-09-05 10:59:44,159][273146] Updated weights for policy 0, policy_version 180 (0.0022) [2023-09-05 10:59:44,334][272918] Fps is (10 sec: 13926.5, 60 sec: 12288.1, 300 sec: 12114.2). Total num frames: 737280. Throughput: 0: 3316.6. Samples: 185138. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) [2023-09-05 10:59:44,335][272918] Avg episode reward: [(0, '5.000')] [2023-09-05 10:59:44,335][273075] Saving new best policy, reward=5.000! [2023-09-05 10:59:47,218][273146] Updated weights for policy 0, policy_version 190 (0.0022) [2023-09-05 10:59:49,335][272918] Fps is (10 sec: 13516.8, 60 sec: 13312.0, 300 sec: 12189.6). Total num frames: 802816. Throughput: 0: 3323.1. Samples: 195294. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) [2023-09-05 10:59:49,335][272918] Avg episode reward: [(0, '5.285')] [2023-09-05 10:59:49,365][273075] Saving new best policy, reward=5.285! [2023-09-05 10:59:50,372][273146] Updated weights for policy 0, policy_version 200 (0.0024) [2023-09-05 10:59:53,644][273146] Updated weights for policy 0, policy_version 210 (0.0020) [2023-09-05 10:59:54,335][272918] Fps is (10 sec: 13107.1, 60 sec: 13312.0, 300 sec: 12254.3). Total num frames: 868352. Throughput: 0: 3302.3. Samples: 214700. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-09-05 10:59:54,335][272918] Avg episode reward: [(0, '4.984')] [2023-09-05 10:59:56,640][273146] Updated weights for policy 0, policy_version 220 (0.0021) [2023-09-05 10:59:59,335][272918] Fps is (10 sec: 13107.2, 60 sec: 13243.7, 300 sec: 12310.5). Total num frames: 933888. Throughput: 0: 3306.0. Samples: 234894. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 10:59:59,335][272918] Avg episode reward: [(0, '5.508')] [2023-09-05 10:59:59,343][273075] Saving new best policy, reward=5.508! [2023-09-05 10:59:59,720][273146] Updated weights for policy 0, policy_version 230 (0.0024) [2023-09-05 11:00:02,687][273146] Updated weights for policy 0, policy_version 240 (0.0023) [2023-09-05 11:00:04,334][272918] Fps is (10 sec: 13107.3, 60 sec: 13243.7, 300 sec: 12359.8). Total num frames: 999424. Throughput: 0: 3307.4. Samples: 244988. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2023-09-05 11:00:04,335][272918] Avg episode reward: [(0, '5.247')] [2023-09-05 11:00:05,979][273146] Updated weights for policy 0, policy_version 250 (0.0019) [2023-09-05 11:00:08,952][273146] Updated weights for policy 0, policy_version 260 (0.0020) [2023-09-05 11:00:09,335][272918] Fps is (10 sec: 13516.8, 60 sec: 13243.7, 300 sec: 12451.0). Total num frames: 1069056. Throughput: 0: 3330.0. Samples: 264696. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-09-05 11:00:09,335][272918] Avg episode reward: [(0, '5.880')] [2023-09-05 11:00:09,341][273075] Saving new best policy, reward=5.880! [2023-09-05 11:00:12,025][273146] Updated weights for policy 0, policy_version 270 (0.0028) [2023-09-05 11:00:14,335][272918] Fps is (10 sec: 13516.7, 60 sec: 13243.7, 300 sec: 12487.1). Total num frames: 1134592. Throughput: 0: 3347.6. Samples: 284902. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-09-05 11:00:14,335][272918] Avg episode reward: [(0, '6.638')] [2023-09-05 11:00:14,336][273075] Saving new best policy, reward=6.638! [2023-09-05 11:00:15,077][273146] Updated weights for policy 0, policy_version 280 (0.0022) [2023-09-05 11:00:18,087][273146] Updated weights for policy 0, policy_version 290 (0.0019) [2023-09-05 11:00:19,335][272918] Fps is (10 sec: 13106.8, 60 sec: 13243.7, 300 sec: 12519.4). Total num frames: 1200128. Throughput: 0: 3351.6. Samples: 295020. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-09-05 11:00:19,335][272918] Avg episode reward: [(0, '6.295')] [2023-09-05 11:00:21,249][273146] Updated weights for policy 0, policy_version 300 (0.0024) [2023-09-05 11:00:24,280][273146] Updated weights for policy 0, policy_version 310 (0.0026) [2023-09-05 11:00:24,335][272918] Fps is (10 sec: 13516.8, 60 sec: 13380.3, 300 sec: 12589.2). Total num frames: 1269760. Throughput: 0: 3333.8. Samples: 314972. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) [2023-09-05 11:00:24,335][272918] Avg episode reward: [(0, '6.793')] [2023-09-05 11:00:24,336][273075] Saving new best policy, reward=6.793! [2023-09-05 11:00:27,263][273146] Updated weights for policy 0, policy_version 320 (0.0021) [2023-09-05 11:00:29,335][272918] Fps is (10 sec: 13517.1, 60 sec: 13380.3, 300 sec: 12613.7). Total num frames: 1335296. Throughput: 0: 3336.7. Samples: 335288. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:00:29,335][272918] Avg episode reward: [(0, '7.181')] [2023-09-05 11:00:29,354][273075] Saving /media/ml1/data/nogletrading/ppo_vizdoom/train_dir/default_experiment/checkpoint_p0/checkpoint_000000327_1339392.pth... [2023-09-05 11:00:29,425][273075] Saving new best policy, reward=7.181! [2023-09-05 11:00:30,346][273146] Updated weights for policy 0, policy_version 330 (0.0024) [2023-09-05 11:00:33,428][273146] Updated weights for policy 0, policy_version 340 (0.0021) [2023-09-05 11:00:34,334][272918] Fps is (10 sec: 13107.3, 60 sec: 13380.3, 300 sec: 12636.0). Total num frames: 1400832. Throughput: 0: 3333.3. Samples: 345290. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-09-05 11:00:34,335][272918] Avg episode reward: [(0, '8.376')] [2023-09-05 11:00:34,355][273075] Saving new best policy, reward=8.376! [2023-09-05 11:00:36,545][273146] Updated weights for policy 0, policy_version 350 (0.0026) [2023-09-05 11:00:39,335][272918] Fps is (10 sec: 13516.7, 60 sec: 13380.2, 300 sec: 12691.6). Total num frames: 1470464. Throughput: 0: 3344.1. Samples: 365184. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:00:39,335][272918] Avg episode reward: [(0, '8.591')] [2023-09-05 11:00:39,342][273075] Saving new best policy, reward=8.591! [2023-09-05 11:00:39,554][273146] Updated weights for policy 0, policy_version 360 (0.0023) [2023-09-05 11:00:42,541][273146] Updated weights for policy 0, policy_version 370 (0.0022) [2023-09-05 11:00:44,335][272918] Fps is (10 sec: 13516.6, 60 sec: 13312.0, 300 sec: 12708.8). Total num frames: 1536000. Throughput: 0: 3345.4. Samples: 385438. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:00:44,335][272918] Avg episode reward: [(0, '9.990')] [2023-09-05 11:00:44,336][273075] Saving new best policy, reward=9.990! [2023-09-05 11:00:45,608][273146] Updated weights for policy 0, policy_version 380 (0.0025) [2023-09-05 11:00:48,766][273146] Updated weights for policy 0, policy_version 390 (0.0020) [2023-09-05 11:00:49,335][272918] Fps is (10 sec: 13107.3, 60 sec: 13312.0, 300 sec: 12724.6). Total num frames: 1601536. Throughput: 0: 3341.8. Samples: 395368. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-09-05 11:00:49,335][272918] Avg episode reward: [(0, '11.946')] [2023-09-05 11:00:49,340][273075] Saving new best policy, reward=11.946! [2023-09-05 11:00:51,861][273146] Updated weights for policy 0, policy_version 400 (0.0020) [2023-09-05 11:00:54,335][272918] Fps is (10 sec: 13516.7, 60 sec: 13380.2, 300 sec: 12770.6). Total num frames: 1671168. Throughput: 0: 3345.3. Samples: 415234. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:00:54,335][272918] Avg episode reward: [(0, '13.390')] [2023-09-05 11:00:54,336][273075] Saving new best policy, reward=13.390! [2023-09-05 11:00:54,888][273146] Updated weights for policy 0, policy_version 410 (0.0020) [2023-09-05 11:00:57,900][273146] Updated weights for policy 0, policy_version 420 (0.0021) [2023-09-05 11:00:59,335][272918] Fps is (10 sec: 13516.7, 60 sec: 13380.3, 300 sec: 12783.0). Total num frames: 1736704. Throughput: 0: 3346.1. Samples: 435478. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:00:59,335][272918] Avg episode reward: [(0, '14.793')] [2023-09-05 11:00:59,341][273075] Saving new best policy, reward=14.793! [2023-09-05 11:01:00,923][273146] Updated weights for policy 0, policy_version 430 (0.0022) [2023-09-05 11:01:04,200][273146] Updated weights for policy 0, policy_version 440 (0.0022) [2023-09-05 11:01:04,335][272918] Fps is (10 sec: 13107.2, 60 sec: 13380.2, 300 sec: 12794.5). Total num frames: 1802240. Throughput: 0: 3341.1. Samples: 445370. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:01:04,335][272918] Avg episode reward: [(0, '13.747')] [2023-09-05 11:01:07,432][273146] Updated weights for policy 0, policy_version 450 (0.0024) [2023-09-05 11:01:09,335][272918] Fps is (10 sec: 13107.3, 60 sec: 13312.0, 300 sec: 12805.2). Total num frames: 1867776. Throughput: 0: 3317.2. Samples: 464246. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:01:09,335][272918] Avg episode reward: [(0, '15.855')] [2023-09-05 11:01:09,341][273075] Saving new best policy, reward=15.855! [2023-09-05 11:01:10,541][273146] Updated weights for policy 0, policy_version 460 (0.0021) [2023-09-05 11:01:13,527][273146] Updated weights for policy 0, policy_version 470 (0.0021) [2023-09-05 11:01:14,335][272918] Fps is (10 sec: 13107.4, 60 sec: 13312.0, 300 sec: 12815.2). Total num frames: 1933312. Throughput: 0: 3315.0. Samples: 484462. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) [2023-09-05 11:01:14,335][272918] Avg episode reward: [(0, '18.685')] [2023-09-05 11:01:14,336][273075] Saving new best policy, reward=18.685! [2023-09-05 11:01:16,623][273146] Updated weights for policy 0, policy_version 480 (0.0021) [2023-09-05 11:01:19,335][272918] Fps is (10 sec: 13107.2, 60 sec: 13312.1, 300 sec: 12824.6). Total num frames: 1998848. Throughput: 0: 3315.1. Samples: 494468. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:01:19,335][272918] Avg episode reward: [(0, '20.522')] [2023-09-05 11:01:19,343][273075] Saving new best policy, reward=20.522! [2023-09-05 11:01:19,842][273146] Updated weights for policy 0, policy_version 490 (0.0022) [2023-09-05 11:01:22,834][273146] Updated weights for policy 0, policy_version 500 (0.0021) [2023-09-05 11:01:24,334][272918] Fps is (10 sec: 13516.9, 60 sec: 13312.0, 300 sec: 12858.8). Total num frames: 2068480. Throughput: 0: 3311.8. Samples: 514212. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:01:24,335][272918] Avg episode reward: [(0, '21.052')] [2023-09-05 11:01:24,336][273075] Saving new best policy, reward=21.052! [2023-09-05 11:01:25,844][273146] Updated weights for policy 0, policy_version 510 (0.0025) [2023-09-05 11:01:28,874][273146] Updated weights for policy 0, policy_version 520 (0.0025) [2023-09-05 11:01:29,335][272918] Fps is (10 sec: 13516.8, 60 sec: 13312.0, 300 sec: 12866.3). Total num frames: 2134016. Throughput: 0: 3311.5. Samples: 534454. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:01:29,335][272918] Avg episode reward: [(0, '18.649')] [2023-09-05 11:01:31,946][273146] Updated weights for policy 0, policy_version 530 (0.0020) [2023-09-05 11:01:34,335][272918] Fps is (10 sec: 13107.1, 60 sec: 13312.0, 300 sec: 12873.4). Total num frames: 2199552. Throughput: 0: 3311.8. Samples: 544398. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2023-09-05 11:01:34,335][272918] Avg episode reward: [(0, '18.016')] [2023-09-05 11:01:35,077][273146] Updated weights for policy 0, policy_version 540 (0.0026) [2023-09-05 11:01:38,046][273146] Updated weights for policy 0, policy_version 550 (0.0020) [2023-09-05 11:01:39,335][272918] Fps is (10 sec: 13516.8, 60 sec: 13312.0, 300 sec: 12903.3). Total num frames: 2269184. Throughput: 0: 3318.1. Samples: 564548. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2023-09-05 11:01:39,335][272918] Avg episode reward: [(0, '17.439')] [2023-09-05 11:01:41,126][273146] Updated weights for policy 0, policy_version 560 (0.0026) [2023-09-05 11:01:44,116][273146] Updated weights for policy 0, policy_version 570 (0.0022) [2023-09-05 11:01:44,335][272918] Fps is (10 sec: 13516.8, 60 sec: 13312.0, 300 sec: 12908.9). Total num frames: 2334720. Throughput: 0: 3318.9. Samples: 584830. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-09-05 11:01:44,335][272918] Avg episode reward: [(0, '20.176')] [2023-09-05 11:01:47,095][273146] Updated weights for policy 0, policy_version 580 (0.0022) [2023-09-05 11:01:49,335][272918] Fps is (10 sec: 13516.8, 60 sec: 13380.3, 300 sec: 12936.3). Total num frames: 2404352. Throughput: 0: 3330.1. Samples: 595226. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:01:49,335][272918] Avg episode reward: [(0, '22.372')] [2023-09-05 11:01:49,343][273075] Saving new best policy, reward=22.372! [2023-09-05 11:01:50,071][273146] Updated weights for policy 0, policy_version 590 (0.0024) [2023-09-05 11:01:53,092][273146] Updated weights for policy 0, policy_version 600 (0.0024) [2023-09-05 11:01:54,335][272918] Fps is (10 sec: 13926.5, 60 sec: 13380.3, 300 sec: 12962.2). Total num frames: 2473984. Throughput: 0: 3366.9. Samples: 615756. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2023-09-05 11:01:54,335][272918] Avg episode reward: [(0, '21.832')] [2023-09-05 11:01:56,097][273146] Updated weights for policy 0, policy_version 610 (0.0019) [2023-09-05 11:01:59,149][273146] Updated weights for policy 0, policy_version 620 (0.0022) [2023-09-05 11:01:59,335][272918] Fps is (10 sec: 13516.8, 60 sec: 13380.3, 300 sec: 12965.9). Total num frames: 2539520. Throughput: 0: 3369.6. Samples: 636096. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:01:59,335][272918] Avg episode reward: [(0, '18.456')] [2023-09-05 11:02:02,099][273146] Updated weights for policy 0, policy_version 630 (0.0027) [2023-09-05 11:02:04,335][272918] Fps is (10 sec: 13107.2, 60 sec: 13380.3, 300 sec: 12969.5). Total num frames: 2605056. Throughput: 0: 3374.4. Samples: 646314. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) [2023-09-05 11:02:04,335][272918] Avg episode reward: [(0, '18.894')] [2023-09-05 11:02:05,393][273146] Updated weights for policy 0, policy_version 640 (0.0025) [2023-09-05 11:02:08,452][273146] Updated weights for policy 0, policy_version 650 (0.0021) [2023-09-05 11:02:09,334][272918] Fps is (10 sec: 13107.5, 60 sec: 13380.3, 300 sec: 12972.8). Total num frames: 2670592. Throughput: 0: 3369.4. Samples: 665836. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-09-05 11:02:09,335][272918] Avg episode reward: [(0, '18.514')] [2023-09-05 11:02:11,476][273146] Updated weights for policy 0, policy_version 660 (0.0023) [2023-09-05 11:02:14,335][272918] Fps is (10 sec: 13516.8, 60 sec: 13448.5, 300 sec: 12995.4). Total num frames: 2740224. Throughput: 0: 3369.8. Samples: 686096. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-09-05 11:02:14,335][272918] Avg episode reward: [(0, '20.559')] [2023-09-05 11:02:14,497][273146] Updated weights for policy 0, policy_version 670 (0.0022) [2023-09-05 11:02:17,491][273146] Updated weights for policy 0, policy_version 680 (0.0023) [2023-09-05 11:02:19,335][272918] Fps is (10 sec: 13926.1, 60 sec: 13516.8, 300 sec: 13017.0). Total num frames: 2809856. Throughput: 0: 3377.9. Samples: 696404. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:02:19,335][272918] Avg episode reward: [(0, '22.185')] [2023-09-05 11:02:20,481][273146] Updated weights for policy 0, policy_version 690 (0.0021) [2023-09-05 11:02:23,472][273146] Updated weights for policy 0, policy_version 700 (0.0022) [2023-09-05 11:02:24,335][272918] Fps is (10 sec: 13516.8, 60 sec: 13448.5, 300 sec: 13019.0). Total num frames: 2875392. Throughput: 0: 3382.6. Samples: 716764. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:02:24,336][272918] Avg episode reward: [(0, '23.435')] [2023-09-05 11:02:24,337][273075] Saving new best policy, reward=23.435! [2023-09-05 11:02:26,545][273146] Updated weights for policy 0, policy_version 710 (0.0024) [2023-09-05 11:02:29,335][272918] Fps is (10 sec: 13516.8, 60 sec: 13516.8, 300 sec: 13039.1). Total num frames: 2945024. Throughput: 0: 3381.4. Samples: 736992. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) [2023-09-05 11:02:29,336][272918] Avg episode reward: [(0, '24.806')] [2023-09-05 11:02:29,344][273075] Saving /media/ml1/data/nogletrading/ppo_vizdoom/train_dir/default_experiment/checkpoint_p0/checkpoint_000000719_2945024.pth... [2023-09-05 11:02:29,414][273075] Saving new best policy, reward=24.806! [2023-09-05 11:02:29,608][273146] Updated weights for policy 0, policy_version 720 (0.0020) [2023-09-05 11:02:32,750][273146] Updated weights for policy 0, policy_version 730 (0.0020) [2023-09-05 11:02:34,335][272918] Fps is (10 sec: 13516.8, 60 sec: 13516.8, 300 sec: 13040.6). Total num frames: 3010560. Throughput: 0: 3364.9. Samples: 746644. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-09-05 11:02:34,335][272918] Avg episode reward: [(0, '23.367')] [2023-09-05 11:02:35,757][273146] Updated weights for policy 0, policy_version 740 (0.0026) [2023-09-05 11:02:38,889][273146] Updated weights for policy 0, policy_version 750 (0.0024) [2023-09-05 11:02:39,335][272918] Fps is (10 sec: 13107.3, 60 sec: 13448.5, 300 sec: 13042.0). Total num frames: 3076096. Throughput: 0: 3356.6. Samples: 766804. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:02:39,335][272918] Avg episode reward: [(0, '23.194')] [2023-09-05 11:02:41,880][273146] Updated weights for policy 0, policy_version 760 (0.0022) [2023-09-05 11:02:44,334][272918] Fps is (10 sec: 13517.0, 60 sec: 13516.8, 300 sec: 13060.4). Total num frames: 3145728. Throughput: 0: 3356.3. Samples: 787128. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-09-05 11:02:44,335][272918] Avg episode reward: [(0, '24.675')] [2023-09-05 11:02:44,885][273146] Updated weights for policy 0, policy_version 770 (0.0024) [2023-09-05 11:02:47,776][273146] Updated weights for policy 0, policy_version 780 (0.0018) [2023-09-05 11:02:49,335][272918] Fps is (10 sec: 13926.4, 60 sec: 13516.8, 300 sec: 13078.0). Total num frames: 3215360. Throughput: 0: 3365.2. Samples: 797750. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-09-05 11:02:49,335][272918] Avg episode reward: [(0, '21.628')] [2023-09-05 11:02:50,793][273146] Updated weights for policy 0, policy_version 790 (0.0024) [2023-09-05 11:02:53,815][273146] Updated weights for policy 0, policy_version 800 (0.0021) [2023-09-05 11:02:54,335][272918] Fps is (10 sec: 13516.6, 60 sec: 13448.5, 300 sec: 13078.6). Total num frames: 3280896. Throughput: 0: 3384.3. Samples: 818128. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:02:54,335][272918] Avg episode reward: [(0, '23.257')] [2023-09-05 11:02:56,820][273146] Updated weights for policy 0, policy_version 810 (0.0028) [2023-09-05 11:02:59,335][272918] Fps is (10 sec: 13516.9, 60 sec: 13516.8, 300 sec: 13095.1). Total num frames: 3350528. Throughput: 0: 3381.9. Samples: 838280. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:02:59,335][272918] Avg episode reward: [(0, '25.138')] [2023-09-05 11:02:59,344][273075] Saving new best policy, reward=25.138! [2023-09-05 11:02:59,945][273146] Updated weights for policy 0, policy_version 820 (0.0021) [2023-09-05 11:03:03,134][273146] Updated weights for policy 0, policy_version 830 (0.0021) [2023-09-05 11:03:04,335][272918] Fps is (10 sec: 13107.1, 60 sec: 13448.5, 300 sec: 13079.6). Total num frames: 3411968. Throughput: 0: 3361.8. Samples: 847684. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2023-09-05 11:03:04,335][272918] Avg episode reward: [(0, '25.295')] [2023-09-05 11:03:04,336][273075] Saving new best policy, reward=25.295! [2023-09-05 11:03:06,301][273146] Updated weights for policy 0, policy_version 840 (0.0022) [2023-09-05 11:03:09,286][273146] Updated weights for policy 0, policy_version 850 (0.0023) [2023-09-05 11:03:09,335][272918] Fps is (10 sec: 13107.2, 60 sec: 13516.8, 300 sec: 13095.6). Total num frames: 3481600. Throughput: 0: 3353.8. Samples: 867686. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2023-09-05 11:03:09,335][272918] Avg episode reward: [(0, '23.496')] [2023-09-05 11:03:12,386][273146] Updated weights for policy 0, policy_version 860 (0.0022) [2023-09-05 11:03:14,335][272918] Fps is (10 sec: 13107.3, 60 sec: 13380.3, 300 sec: 13080.7). Total num frames: 3543040. Throughput: 0: 3333.9. Samples: 887018. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:03:14,335][272918] Avg episode reward: [(0, '23.233')] [2023-09-05 11:03:15,630][273146] Updated weights for policy 0, policy_version 870 (0.0024) [2023-09-05 11:03:18,835][273146] Updated weights for policy 0, policy_version 880 (0.0023) [2023-09-05 11:03:19,335][272918] Fps is (10 sec: 12697.5, 60 sec: 13312.0, 300 sec: 13081.1). Total num frames: 3608576. Throughput: 0: 3341.8. Samples: 897026. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:03:19,335][272918] Avg episode reward: [(0, '24.170')] [2023-09-05 11:03:21,975][273146] Updated weights for policy 0, policy_version 890 (0.0023) [2023-09-05 11:03:24,335][272918] Fps is (10 sec: 13107.2, 60 sec: 13312.0, 300 sec: 13081.6). Total num frames: 3674112. Throughput: 0: 3324.8. Samples: 916418. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:03:24,335][272918] Avg episode reward: [(0, '24.026')] [2023-09-05 11:03:25,044][273146] Updated weights for policy 0, policy_version 900 (0.0021) [2023-09-05 11:03:28,101][273146] Updated weights for policy 0, policy_version 910 (0.0021) [2023-09-05 11:03:29,334][272918] Fps is (10 sec: 13517.0, 60 sec: 13312.0, 300 sec: 13096.4). Total num frames: 3743744. Throughput: 0: 3319.3. Samples: 936496. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-09-05 11:03:29,335][272918] Avg episode reward: [(0, '23.463')] [2023-09-05 11:03:31,228][273146] Updated weights for policy 0, policy_version 920 (0.0024) [2023-09-05 11:03:34,274][273146] Updated weights for policy 0, policy_version 930 (0.0026) [2023-09-05 11:03:34,335][272918] Fps is (10 sec: 13516.8, 60 sec: 13312.0, 300 sec: 13096.6). Total num frames: 3809280. Throughput: 0: 3301.7. Samples: 946328. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) [2023-09-05 11:03:34,335][272918] Avg episode reward: [(0, '25.828')] [2023-09-05 11:03:34,336][273075] Saving new best policy, reward=25.828! [2023-09-05 11:03:37,349][273146] Updated weights for policy 0, policy_version 940 (0.0022) [2023-09-05 11:03:39,335][272918] Fps is (10 sec: 13107.0, 60 sec: 13312.0, 300 sec: 13135.0). Total num frames: 3874816. Throughput: 0: 3297.1. Samples: 966496. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-09-05 11:03:39,335][272918] Avg episode reward: [(0, '25.149')] [2023-09-05 11:03:40,415][273146] Updated weights for policy 0, policy_version 950 (0.0020) [2023-09-05 11:03:43,469][273146] Updated weights for policy 0, policy_version 960 (0.0023) [2023-09-05 11:03:44,335][272918] Fps is (10 sec: 13107.2, 60 sec: 13243.7, 300 sec: 13343.2). Total num frames: 3940352. Throughput: 0: 3294.0. Samples: 986512. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-09-05 11:03:44,335][272918] Avg episode reward: [(0, '25.829')] [2023-09-05 11:03:44,374][273075] Saving new best policy, reward=25.829! [2023-09-05 11:03:46,544][273146] Updated weights for policy 0, policy_version 970 (0.0024) [2023-09-05 11:03:48,986][273075] Stopping Batcher_0... [2023-09-05 11:03:48,987][273075] Loop batcher_evt_loop terminating... [2023-09-05 11:03:48,986][272918] Component Batcher_0 stopped! [2023-09-05 11:03:48,989][273075] Saving /media/ml1/data/nogletrading/ppo_vizdoom/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... [2023-09-05 11:03:48,998][272918] Component RolloutWorker_w7 stopped! [2023-09-05 11:03:49,001][272918] Component RolloutWorker_w5 stopped! [2023-09-05 11:03:48,999][273160] Stopping RolloutWorker_w7... [2023-09-05 11:03:49,000][273164] Stopping RolloutWorker_w5... [2023-09-05 11:03:49,002][272918] Component RolloutWorker_w2 stopped! [2023-09-05 11:03:49,002][273148] Stopping RolloutWorker_w2... [2023-09-05 11:03:49,003][273160] Loop rollout_proc7_evt_loop terminating... [2023-09-05 11:03:49,005][272918] Component RolloutWorker_w3 stopped! [2023-09-05 11:03:49,002][273164] Loop rollout_proc5_evt_loop terminating... [2023-09-05 11:03:49,003][273148] Loop rollout_proc2_evt_loop terminating... [2023-09-05 11:03:49,005][273162] Stopping RolloutWorker_w3... [2023-09-05 11:03:49,007][273162] Loop rollout_proc3_evt_loop terminating... [2023-09-05 11:03:49,007][273146] Weights refcount: 2 0 [2023-09-05 11:03:49,010][272918] Component RolloutWorker_w1 stopped! [2023-09-05 11:03:49,010][273149] Stopping RolloutWorker_w1... [2023-09-05 11:03:49,011][273149] Loop rollout_proc1_evt_loop terminating... [2023-09-05 11:03:49,017][272918] Component InferenceWorker_p0-w0 stopped! [2023-09-05 11:03:49,017][273146] Stopping InferenceWorker_p0-w0... [2023-09-05 11:03:49,020][272918] Component RolloutWorker_w0 stopped! [2023-09-05 11:03:49,020][273147] Stopping RolloutWorker_w0... [2023-09-05 11:03:49,022][273147] Loop rollout_proc0_evt_loop terminating... [2023-09-05 11:03:49,024][273146] Loop inference_proc0-0_evt_loop terminating... [2023-09-05 11:03:49,026][273157] Stopping RolloutWorker_w4... [2023-09-05 11:03:49,026][272918] Component RolloutWorker_w4 stopped! [2023-09-05 11:03:49,027][273157] Loop rollout_proc4_evt_loop terminating... [2023-09-05 11:03:49,029][272918] Component RolloutWorker_w6 stopped! [2023-09-05 11:03:49,029][273165] Stopping RolloutWorker_w6... [2023-09-05 11:03:49,030][273165] Loop rollout_proc6_evt_loop terminating... [2023-09-05 11:03:49,049][273075] Removing /media/ml1/data/nogletrading/ppo_vizdoom/train_dir/default_experiment/checkpoint_p0/checkpoint_000000327_1339392.pth [2023-09-05 11:03:49,054][273075] Saving new best policy, reward=26.360! [2023-09-05 11:03:49,113][273075] Saving /media/ml1/data/nogletrading/ppo_vizdoom/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... [2023-09-05 11:03:49,384][273075] Stopping LearnerWorker_p0... [2023-09-05 11:03:49,384][272918] Component LearnerWorker_p0 stopped! [2023-09-05 11:03:49,385][272918] Waiting for process learner_proc0 to stop... [2023-09-05 11:03:49,385][273075] Loop learner_proc0_evt_loop terminating... [2023-09-05 11:03:50,689][272918] Waiting for process inference_proc0-0 to join... [2023-09-05 11:03:50,689][272918] Waiting for process rollout_proc0 to join... [2023-09-05 11:03:50,690][272918] Waiting for process rollout_proc1 to join... [2023-09-05 11:03:50,690][272918] Waiting for process rollout_proc2 to join... [2023-09-05 11:03:50,690][272918] Waiting for process rollout_proc3 to join... [2023-09-05 11:03:50,690][272918] Waiting for process rollout_proc4 to join... [2023-09-05 11:03:50,690][272918] Waiting for process rollout_proc5 to join... [2023-09-05 11:03:50,691][272918] Waiting for process rollout_proc6 to join... [2023-09-05 11:03:50,691][272918] Waiting for process rollout_proc7 to join... [2023-09-05 11:03:50,691][272918] Batcher 0 profile tree view: batching: 12.1773, releasing_batches: 0.0370 [2023-09-05 11:03:50,692][272918] InferenceWorker_p0-w0 profile tree view: wait_policy: 0.0002 wait_policy_total: 8.1322 update_model: 6.1108 weight_update: 0.0024 one_step: 0.0040 handle_policy_step: 259.8800 deserialize: 12.6613, stack: 2.4922, obs_to_device_normalize: 69.9636, forward: 100.9362, send_messages: 22.0939 prepare_outputs: 30.9768 to_cpu: 17.8297 [2023-09-05 11:03:50,693][272918] Learner 0 profile tree view: misc: 0.0108, prepare_batch: 8.5503 train: 30.1955 epoch_init: 0.0128, minibatch_init: 0.0083, losses_postprocess: 0.2211, kl_divergence: 0.2575, after_optimizer: 10.6754 calculate_losses: 10.3571 losses_init: 0.0088, forward_head: 0.7368, bptt_initial: 6.7256, tail: 0.5483, advantages_returns: 0.1471, losses: 0.8372 bptt: 1.0653 bptt_forward_core: 0.9959 update: 8.1721 clip: 1.5875 [2023-09-05 11:03:50,693][272918] RolloutWorker_w0 profile tree view: wait_for_trajectories: 0.3348, enqueue_policy_requests: 11.6962, env_step: 124.9929, overhead: 10.9930, complete_rollouts: 0.7431 save_policy_outputs: 23.2284 split_output_tensors: 9.0382 [2023-09-05 11:03:50,693][272918] RolloutWorker_w7 profile tree view: wait_for_trajectories: 0.3340, enqueue_policy_requests: 11.9348, env_step: 126.8656, overhead: 11.2031, complete_rollouts: 0.8436 save_policy_outputs: 22.9981 split_output_tensors: 8.8397 [2023-09-05 11:03:50,694][272918] Loop Runner_EvtLoop terminating... [2023-09-05 11:03:50,694][272918] Runner profile tree view: main_loop: 315.5323 [2023-09-05 11:03:50,695][272918] Collected {0: 4005888}, FPS: 12695.6 |