[2023-02-22 21:22:50,811][24717] Saving configuration to /home/flahoud/studies/collab/train_dir/default_experiment/config.json... [2023-02-22 21:22:50,812][24717] Rollout worker 0 uses device cpu [2023-02-22 21:22:50,813][24717] Rollout worker 1 uses device cpu [2023-02-22 21:22:50,813][24717] Rollout worker 2 uses device cpu [2023-02-22 21:22:50,814][24717] Rollout worker 3 uses device cpu [2023-02-22 21:22:50,815][24717] Rollout worker 4 uses device cpu [2023-02-22 21:22:50,815][24717] Rollout worker 5 uses device cpu [2023-02-22 21:22:50,816][24717] Rollout worker 6 uses device cpu [2023-02-22 21:22:50,817][24717] Rollout worker 7 uses device cpu [2023-02-22 21:22:50,874][24717] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2023-02-22 21:22:50,875][24717] InferenceWorker_p0-w0: min num requests: 2 [2023-02-22 21:22:50,907][24717] Starting all processes... [2023-02-22 21:22:50,908][24717] Starting process learner_proc0 [2023-02-22 21:22:50,957][24717] Starting all processes... [2023-02-22 21:22:50,964][24717] Starting process inference_proc0-0 [2023-02-22 21:22:50,965][24717] Starting process rollout_proc0 [2023-02-22 21:22:50,965][24717] Starting process rollout_proc1 [2023-02-22 21:22:50,966][24717] Starting process rollout_proc2 [2023-02-22 21:22:50,967][24717] Starting process rollout_proc3 [2023-02-22 21:22:50,967][24717] Starting process rollout_proc4 [2023-02-22 21:22:50,968][24717] Starting process rollout_proc5 [2023-02-22 21:22:50,968][24717] Starting process rollout_proc6 [2023-02-22 21:22:50,968][24717] Starting process rollout_proc7 [2023-02-22 21:22:52,699][32247] Worker 1 uses CPU cores [1] [2023-02-22 21:22:52,745][32230] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2023-02-22 21:22:52,745][32230] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0 [2023-02-22 21:22:52,758][32230] Num visible devices: 1 [2023-02-22 21:22:52,803][32230] Starting seed is not provided [2023-02-22 21:22:52,803][32230] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2023-02-22 21:22:52,803][32230] Initializing actor-critic model on device cuda:0 [2023-02-22 21:22:52,803][32230] RunningMeanStd input shape: (3, 72, 128) [2023-02-22 21:22:52,804][32230] RunningMeanStd input shape: (1,) [2023-02-22 21:22:52,815][32230] ConvEncoder: input_channels=3 [2023-02-22 21:22:52,855][32246] Worker 0 uses CPU cores [0] [2023-02-22 21:22:52,864][32245] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2023-02-22 21:22:52,864][32245] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0 [2023-02-22 21:22:52,878][32245] Num visible devices: 1 [2023-02-22 21:22:52,943][32249] Worker 3 uses CPU cores [3] [2023-02-22 21:22:52,955][32253] Worker 4 uses CPU cores [4] [2023-02-22 21:22:52,969][32230] Conv encoder output size: 512 [2023-02-22 21:22:52,970][32230] Policy head output size: 512 [2023-02-22 21:22:52,983][32230] Created Actor Critic model with architecture: [2023-02-22 21:22:52,984][32230] 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) ) ) [2023-02-22 21:22:52,989][32262] Worker 6 uses CPU cores [6] [2023-02-22 21:22:53,089][32248] Worker 2 uses CPU cores [2] [2023-02-22 21:22:53,090][32252] Worker 5 uses CPU cores [5] [2023-02-22 21:22:53,203][32263] Worker 7 uses CPU cores [7] [2023-02-22 21:22:55,724][32230] Using optimizer [2023-02-22 21:22:55,725][32230] No checkpoints found [2023-02-22 21:22:55,725][32230] Did not load from checkpoint, starting from scratch! [2023-02-22 21:22:55,725][32230] Initialized policy 0 weights for model version 0 [2023-02-22 21:22:55,727][32230] LearnerWorker_p0 finished initialization! [2023-02-22 21:22:55,728][32230] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2023-02-22 21:22:55,918][32245] RunningMeanStd input shape: (3, 72, 128) [2023-02-22 21:22:55,919][32245] RunningMeanStd input shape: (1,) [2023-02-22 21:22:55,929][32245] ConvEncoder: input_channels=3 [2023-02-22 21:22:56,020][32245] Conv encoder output size: 512 [2023-02-22 21:22:56,021][32245] Policy head output size: 512 [2023-02-22 21:22:58,156][24717] 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) [2023-02-22 21:22:58,538][24717] Inference worker 0-0 is ready! [2023-02-22 21:22:58,539][24717] All inference workers are ready! Signal rollout workers to start! [2023-02-22 21:22:58,557][32247] Doom resolution: 160x120, resize resolution: (128, 72) [2023-02-22 21:22:58,558][32262] Doom resolution: 160x120, resize resolution: (128, 72) [2023-02-22 21:22:58,558][32263] Doom resolution: 160x120, resize resolution: (128, 72) [2023-02-22 21:22:58,558][32248] Doom resolution: 160x120, resize resolution: (128, 72) [2023-02-22 21:22:58,558][32246] Doom resolution: 160x120, resize resolution: (128, 72) [2023-02-22 21:22:58,559][32253] Doom resolution: 160x120, resize resolution: (128, 72) [2023-02-22 21:22:58,560][32249] Doom resolution: 160x120, resize resolution: (128, 72) [2023-02-22 21:22:58,579][32252] Doom resolution: 160x120, resize resolution: (128, 72) [2023-02-22 21:22:59,188][32247] Decorrelating experience for 0 frames... [2023-02-22 21:22:59,191][32246] Decorrelating experience for 0 frames... [2023-02-22 21:22:59,192][32249] Decorrelating experience for 0 frames... [2023-02-22 21:22:59,193][32252] Decorrelating experience for 0 frames... [2023-02-22 21:22:59,194][32262] Decorrelating experience for 0 frames... [2023-02-22 21:22:59,195][32263] Decorrelating experience for 0 frames... [2023-02-22 21:22:59,738][32249] Decorrelating experience for 32 frames... [2023-02-22 21:22:59,739][32263] Decorrelating experience for 32 frames... [2023-02-22 21:22:59,739][32246] Decorrelating experience for 32 frames... [2023-02-22 21:22:59,740][32253] Decorrelating experience for 0 frames... [2023-02-22 21:22:59,744][32248] Decorrelating experience for 0 frames... [2023-02-22 21:22:59,745][32247] Decorrelating experience for 32 frames... [2023-02-22 21:23:00,186][32253] Decorrelating experience for 32 frames... [2023-02-22 21:23:00,320][32252] Decorrelating experience for 32 frames... [2023-02-22 21:23:00,322][32248] Decorrelating experience for 32 frames... [2023-02-22 21:23:00,323][32263] Decorrelating experience for 64 frames... [2023-02-22 21:23:00,324][32246] Decorrelating experience for 64 frames... [2023-02-22 21:23:00,324][32262] Decorrelating experience for 32 frames... [2023-02-22 21:23:00,527][32249] Decorrelating experience for 64 frames... [2023-02-22 21:23:00,770][32246] Decorrelating experience for 96 frames... [2023-02-22 21:23:00,863][32253] Decorrelating experience for 64 frames... [2023-02-22 21:23:00,863][32263] Decorrelating experience for 96 frames... [2023-02-22 21:23:00,863][32252] Decorrelating experience for 64 frames... [2023-02-22 21:23:00,865][32247] Decorrelating experience for 64 frames... [2023-02-22 21:23:01,383][32252] Decorrelating experience for 96 frames... [2023-02-22 21:23:01,384][32253] Decorrelating experience for 96 frames... [2023-02-22 21:23:01,384][32247] Decorrelating experience for 96 frames... [2023-02-22 21:23:01,386][32249] Decorrelating experience for 96 frames... [2023-02-22 21:23:01,386][32248] Decorrelating experience for 64 frames... [2023-02-22 21:23:01,776][32262] Decorrelating experience for 64 frames... [2023-02-22 21:23:01,777][32248] Decorrelating experience for 96 frames... [2023-02-22 21:23:02,120][32262] Decorrelating experience for 96 frames... [2023-02-22 21:23:02,338][32230] Signal inference workers to stop experience collection... [2023-02-22 21:23:02,342][32245] InferenceWorker_p0-w0: stopping experience collection [2023-02-22 21:23:03,156][24717] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 7.2. Samples: 36. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) [2023-02-22 21:23:03,158][24717] Avg episode reward: [(0, '2.456')] [2023-02-22 21:23:04,256][32230] Signal inference workers to resume experience collection... [2023-02-22 21:23:04,256][32245] InferenceWorker_p0-w0: resuming experience collection [2023-02-22 21:23:06,289][32245] Updated weights for policy 0, policy_version 10 (0.0008) [2023-02-22 21:23:07,913][32245] Updated weights for policy 0, policy_version 20 (0.0010) [2023-02-22 21:23:08,156][24717] Fps is (10 sec: 8601.6, 60 sec: 8601.6, 300 sec: 8601.6). Total num frames: 86016. Throughput: 0: 1899.2. Samples: 18992. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0) [2023-02-22 21:23:08,157][24717] Avg episode reward: [(0, '4.545')] [2023-02-22 21:23:09,552][32245] Updated weights for policy 0, policy_version 30 (0.0007) [2023-02-22 21:23:10,866][24717] Heartbeat connected on Batcher_0 [2023-02-22 21:23:10,869][24717] Heartbeat connected on LearnerWorker_p0 [2023-02-22 21:23:10,881][24717] Heartbeat connected on InferenceWorker_p0-w0 [2023-02-22 21:23:10,887][24717] Heartbeat connected on RolloutWorker_w2 [2023-02-22 21:23:10,890][24717] Heartbeat connected on RolloutWorker_w0 [2023-02-22 21:23:10,892][24717] Heartbeat connected on RolloutWorker_w3 [2023-02-22 21:23:10,893][24717] Heartbeat connected on RolloutWorker_w1 [2023-02-22 21:23:10,898][24717] Heartbeat connected on RolloutWorker_w4 [2023-02-22 21:23:10,900][24717] Heartbeat connected on RolloutWorker_w5 [2023-02-22 21:23:10,905][24717] Heartbeat connected on RolloutWorker_w6 [2023-02-22 21:23:10,907][24717] Heartbeat connected on RolloutWorker_w7 [2023-02-22 21:23:11,206][32245] Updated weights for policy 0, policy_version 40 (0.0007) [2023-02-22 21:23:12,868][32245] Updated weights for policy 0, policy_version 50 (0.0008) [2023-02-22 21:23:13,156][24717] Fps is (10 sec: 20889.3, 60 sec: 13926.3, 300 sec: 13926.3). Total num frames: 208896. Throughput: 0: 2502.7. Samples: 37540. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-02-22 21:23:13,158][24717] Avg episode reward: [(0, '4.397')] [2023-02-22 21:23:13,159][32230] Saving new best policy, reward=4.397! [2023-02-22 21:23:14,551][32245] Updated weights for policy 0, policy_version 60 (0.0007) [2023-02-22 21:23:16,232][32245] Updated weights for policy 0, policy_version 70 (0.0006) [2023-02-22 21:23:17,952][32245] Updated weights for policy 0, policy_version 80 (0.0007) [2023-02-22 21:23:18,156][24717] Fps is (10 sec: 24575.9, 60 sec: 16588.8, 300 sec: 16588.8). Total num frames: 331776. Throughput: 0: 3717.4. Samples: 74348. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-02-22 21:23:18,157][24717] Avg episode reward: [(0, '4.329')] [2023-02-22 21:23:19,740][32245] Updated weights for policy 0, policy_version 90 (0.0010) [2023-02-22 21:23:21,483][32245] Updated weights for policy 0, policy_version 100 (0.0007) [2023-02-22 21:23:23,156][24717] Fps is (10 sec: 23757.1, 60 sec: 17858.6, 300 sec: 17858.6). Total num frames: 446464. Throughput: 0: 4375.5. Samples: 109386. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-02-22 21:23:23,157][24717] Avg episode reward: [(0, '4.485')] [2023-02-22 21:23:23,159][32230] Saving new best policy, reward=4.485! [2023-02-22 21:23:23,248][32245] Updated weights for policy 0, policy_version 110 (0.0009) [2023-02-22 21:23:25,020][32245] Updated weights for policy 0, policy_version 120 (0.0008) [2023-02-22 21:23:26,756][32245] Updated weights for policy 0, policy_version 130 (0.0007) [2023-02-22 21:23:28,156][24717] Fps is (10 sec: 23347.1, 60 sec: 18841.6, 300 sec: 18841.6). Total num frames: 565248. Throughput: 0: 4225.1. Samples: 126752. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-02-22 21:23:28,158][24717] Avg episode reward: [(0, '4.570')] [2023-02-22 21:23:28,162][32230] Saving new best policy, reward=4.570! [2023-02-22 21:23:28,520][32245] Updated weights for policy 0, policy_version 140 (0.0008) [2023-02-22 21:23:30,204][32245] Updated weights for policy 0, policy_version 150 (0.0007) [2023-02-22 21:23:31,950][32245] Updated weights for policy 0, policy_version 160 (0.0008) [2023-02-22 21:23:33,156][24717] Fps is (10 sec: 23756.7, 60 sec: 19543.8, 300 sec: 19543.8). Total num frames: 684032. Throughput: 0: 4632.2. Samples: 162128. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2023-02-22 21:23:33,157][24717] Avg episode reward: [(0, '4.758')] [2023-02-22 21:23:33,159][32230] Saving new best policy, reward=4.758! [2023-02-22 21:23:33,637][32245] Updated weights for policy 0, policy_version 170 (0.0006) [2023-02-22 21:23:35,345][32245] Updated weights for policy 0, policy_version 180 (0.0008) [2023-02-22 21:23:37,053][32245] Updated weights for policy 0, policy_version 190 (0.0007) [2023-02-22 21:23:38,156][24717] Fps is (10 sec: 23757.0, 60 sec: 20070.4, 300 sec: 20070.4). Total num frames: 802816. Throughput: 0: 4956.4. Samples: 198256. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-02-22 21:23:38,158][24717] Avg episode reward: [(0, '5.231')] [2023-02-22 21:23:38,163][32230] Saving new best policy, reward=5.231! [2023-02-22 21:23:38,776][32245] Updated weights for policy 0, policy_version 200 (0.0006) [2023-02-22 21:23:40,492][32245] Updated weights for policy 0, policy_version 210 (0.0008) [2023-02-22 21:23:42,206][32245] Updated weights for policy 0, policy_version 220 (0.0007) [2023-02-22 21:23:43,156][24717] Fps is (10 sec: 23756.8, 60 sec: 20480.0, 300 sec: 20480.0). Total num frames: 921600. Throughput: 0: 4802.7. Samples: 216120. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-02-22 21:23:43,159][24717] Avg episode reward: [(0, '6.078')] [2023-02-22 21:23:43,160][32230] Saving new best policy, reward=6.078! [2023-02-22 21:23:43,956][32245] Updated weights for policy 0, policy_version 230 (0.0010) [2023-02-22 21:23:45,660][32245] Updated weights for policy 0, policy_version 240 (0.0008) [2023-02-22 21:23:47,448][32245] Updated weights for policy 0, policy_version 250 (0.0006) [2023-02-22 21:23:48,156][24717] Fps is (10 sec: 23346.9, 60 sec: 20725.7, 300 sec: 20725.7). Total num frames: 1036288. Throughput: 0: 5590.7. Samples: 251620. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-02-22 21:23:48,161][24717] Avg episode reward: [(0, '6.450')] [2023-02-22 21:23:48,167][32230] Saving new best policy, reward=6.450! [2023-02-22 21:23:49,218][32245] Updated weights for policy 0, policy_version 260 (0.0008) [2023-02-22 21:23:50,966][32245] Updated weights for policy 0, policy_version 270 (0.0012) [2023-02-22 21:23:52,686][32245] Updated weights for policy 0, policy_version 280 (0.0008) [2023-02-22 21:23:53,156][24717] Fps is (10 sec: 23347.1, 60 sec: 21001.3, 300 sec: 21001.3). Total num frames: 1155072. Throughput: 0: 5946.3. Samples: 286574. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-02-22 21:23:53,159][24717] Avg episode reward: [(0, '7.235')] [2023-02-22 21:23:53,160][32230] Saving new best policy, reward=7.235! [2023-02-22 21:23:54,405][32245] Updated weights for policy 0, policy_version 290 (0.0008) [2023-02-22 21:23:56,162][32245] Updated weights for policy 0, policy_version 300 (0.0008) [2023-02-22 21:23:57,856][32245] Updated weights for policy 0, policy_version 310 (0.0007) [2023-02-22 21:23:58,156][24717] Fps is (10 sec: 23757.1, 60 sec: 21230.9, 300 sec: 21230.9). Total num frames: 1273856. Throughput: 0: 5927.5. Samples: 304276. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-02-22 21:23:58,160][24717] Avg episode reward: [(0, '7.943')] [2023-02-22 21:23:58,164][32230] Saving new best policy, reward=7.943! [2023-02-22 21:23:59,584][32245] Updated weights for policy 0, policy_version 320 (0.0006) [2023-02-22 21:24:01,399][32245] Updated weights for policy 0, policy_version 330 (0.0007) [2023-02-22 21:24:03,081][32245] Updated weights for policy 0, policy_version 340 (0.0008) [2023-02-22 21:24:03,156][24717] Fps is (10 sec: 23756.8, 60 sec: 23210.7, 300 sec: 21425.2). Total num frames: 1392640. Throughput: 0: 5894.8. Samples: 339612. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-02-22 21:24:03,158][24717] Avg episode reward: [(0, '9.597')] [2023-02-22 21:24:03,160][32230] Saving new best policy, reward=9.597! [2023-02-22 21:24:04,774][32245] Updated weights for policy 0, policy_version 350 (0.0006) [2023-02-22 21:24:06,472][32245] Updated weights for policy 0, policy_version 360 (0.0007) [2023-02-22 21:24:08,079][32245] Updated weights for policy 0, policy_version 370 (0.0008) [2023-02-22 21:24:08,156][24717] Fps is (10 sec: 24166.4, 60 sec: 23825.1, 300 sec: 21650.3). Total num frames: 1515520. Throughput: 0: 5926.3. Samples: 376070. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-02-22 21:24:08,159][24717] Avg episode reward: [(0, '11.741')] [2023-02-22 21:24:08,164][32230] Saving new best policy, reward=11.741! [2023-02-22 21:24:09,745][32245] Updated weights for policy 0, policy_version 380 (0.0006) [2023-02-22 21:24:11,395][32245] Updated weights for policy 0, policy_version 390 (0.0006) [2023-02-22 21:24:13,047][32245] Updated weights for policy 0, policy_version 400 (0.0007) [2023-02-22 21:24:13,156][24717] Fps is (10 sec: 24576.1, 60 sec: 23825.1, 300 sec: 21845.4). Total num frames: 1638400. Throughput: 0: 5958.9. Samples: 394900. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-02-22 21:24:13,157][24717] Avg episode reward: [(0, '17.552')] [2023-02-22 21:24:13,158][32230] Saving new best policy, reward=17.552! [2023-02-22 21:24:14,722][32245] Updated weights for policy 0, policy_version 410 (0.0006) [2023-02-22 21:24:16,491][32245] Updated weights for policy 0, policy_version 420 (0.0007) [2023-02-22 21:24:18,156][24717] Fps is (10 sec: 24166.4, 60 sec: 23756.8, 300 sec: 21964.8). Total num frames: 1757184. Throughput: 0: 5979.0. Samples: 431182. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-02-22 21:24:18,158][24717] Avg episode reward: [(0, '17.099')] [2023-02-22 21:24:18,175][32245] Updated weights for policy 0, policy_version 430 (0.0008) [2023-02-22 21:24:19,921][32245] Updated weights for policy 0, policy_version 440 (0.0006) [2023-02-22 21:24:21,535][32245] Updated weights for policy 0, policy_version 450 (0.0008) [2023-02-22 21:24:23,156][24717] Fps is (10 sec: 24166.4, 60 sec: 23893.3, 300 sec: 22118.4). Total num frames: 1880064. Throughput: 0: 5985.9. Samples: 467622. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-02-22 21:24:23,157][24717] Avg episode reward: [(0, '16.734')] [2023-02-22 21:24:23,244][32245] Updated weights for policy 0, policy_version 460 (0.0007) [2023-02-22 21:24:24,887][32245] Updated weights for policy 0, policy_version 470 (0.0006) [2023-02-22 21:24:26,622][32245] Updated weights for policy 0, policy_version 480 (0.0006) [2023-02-22 21:24:28,157][24717] Fps is (10 sec: 24575.5, 60 sec: 23961.5, 300 sec: 22254.9). Total num frames: 2002944. Throughput: 0: 5995.7. Samples: 485926. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-02-22 21:24:28,158][24717] Avg episode reward: [(0, '15.807')] [2023-02-22 21:24:28,351][32245] Updated weights for policy 0, policy_version 490 (0.0007) [2023-02-22 21:24:30,011][32245] Updated weights for policy 0, policy_version 500 (0.0008) [2023-02-22 21:24:31,794][32245] Updated weights for policy 0, policy_version 510 (0.0008) [2023-02-22 21:24:33,156][24717] Fps is (10 sec: 24166.3, 60 sec: 23961.6, 300 sec: 22334.0). Total num frames: 2121728. Throughput: 0: 5997.7. Samples: 521516. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-02-22 21:24:33,157][24717] Avg episode reward: [(0, '20.357')] [2023-02-22 21:24:33,159][32230] Saving new best policy, reward=20.357! [2023-02-22 21:24:33,468][32245] Updated weights for policy 0, policy_version 520 (0.0006) [2023-02-22 21:24:35,160][32245] Updated weights for policy 0, policy_version 530 (0.0006) [2023-02-22 21:24:36,867][32245] Updated weights for policy 0, policy_version 540 (0.0007) [2023-02-22 21:24:38,156][24717] Fps is (10 sec: 23757.3, 60 sec: 23961.6, 300 sec: 22405.1). Total num frames: 2240512. Throughput: 0: 6027.9. Samples: 557828. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-02-22 21:24:38,157][24717] Avg episode reward: [(0, '18.501')] [2023-02-22 21:24:38,559][32245] Updated weights for policy 0, policy_version 550 (0.0008) [2023-02-22 21:24:40,271][32245] Updated weights for policy 0, policy_version 560 (0.0008) [2023-02-22 21:24:41,996][32245] Updated weights for policy 0, policy_version 570 (0.0007) [2023-02-22 21:24:43,156][24717] Fps is (10 sec: 24166.5, 60 sec: 24029.9, 300 sec: 22508.5). Total num frames: 2363392. Throughput: 0: 6032.4. Samples: 575736. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-02-22 21:24:43,157][24717] Avg episode reward: [(0, '20.253')] [2023-02-22 21:24:43,651][32245] Updated weights for policy 0, policy_version 580 (0.0006) [2023-02-22 21:24:45,447][32245] Updated weights for policy 0, policy_version 590 (0.0008) [2023-02-22 21:24:47,173][32245] Updated weights for policy 0, policy_version 600 (0.0007) [2023-02-22 21:24:48,156][24717] Fps is (10 sec: 23756.8, 60 sec: 24029.9, 300 sec: 22528.0). Total num frames: 2478080. Throughput: 0: 6041.4. Samples: 611476. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-02-22 21:24:48,160][24717] Avg episode reward: [(0, '19.851')] [2023-02-22 21:24:48,164][32230] Saving /home/flahoud/studies/collab/train_dir/default_experiment/checkpoint_p0/checkpoint_000000605_2478080.pth... [2023-02-22 21:24:48,870][32245] Updated weights for policy 0, policy_version 610 (0.0008) [2023-02-22 21:24:50,606][32245] Updated weights for policy 0, policy_version 620 (0.0009) [2023-02-22 21:24:52,395][32245] Updated weights for policy 0, policy_version 630 (0.0009) [2023-02-22 21:24:53,156][24717] Fps is (10 sec: 23347.2, 60 sec: 24029.9, 300 sec: 22581.4). Total num frames: 2596864. Throughput: 0: 6013.9. Samples: 646694. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-02-22 21:24:53,157][24717] Avg episode reward: [(0, '22.850')] [2023-02-22 21:24:53,160][32230] Saving new best policy, reward=22.850! [2023-02-22 21:24:54,156][32245] Updated weights for policy 0, policy_version 640 (0.0008) [2023-02-22 21:24:55,879][32245] Updated weights for policy 0, policy_version 650 (0.0007) [2023-02-22 21:24:57,621][32245] Updated weights for policy 0, policy_version 660 (0.0007) [2023-02-22 21:24:58,156][24717] Fps is (10 sec: 23756.8, 60 sec: 24029.9, 300 sec: 22630.4). Total num frames: 2715648. Throughput: 0: 5991.9. Samples: 664536. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-02-22 21:24:58,157][24717] Avg episode reward: [(0, '23.369')] [2023-02-22 21:24:58,161][32230] Saving new best policy, reward=23.369! [2023-02-22 21:24:59,373][32245] Updated weights for policy 0, policy_version 670 (0.0008) [2023-02-22 21:25:01,221][32245] Updated weights for policy 0, policy_version 680 (0.0009) [2023-02-22 21:25:02,970][32245] Updated weights for policy 0, policy_version 690 (0.0007) [2023-02-22 21:25:03,156][24717] Fps is (10 sec: 23347.2, 60 sec: 23961.6, 300 sec: 22642.7). Total num frames: 2830336. Throughput: 0: 5951.6. Samples: 699006. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) [2023-02-22 21:25:03,157][24717] Avg episode reward: [(0, '21.691')] [2023-02-22 21:25:04,706][32245] Updated weights for policy 0, policy_version 700 (0.0007) [2023-02-22 21:25:06,440][32245] Updated weights for policy 0, policy_version 710 (0.0007) [2023-02-22 21:25:08,156][24717] Fps is (10 sec: 22937.6, 60 sec: 23825.1, 300 sec: 22654.0). Total num frames: 2945024. Throughput: 0: 5924.6. Samples: 734228. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-02-22 21:25:08,159][24717] Avg episode reward: [(0, '20.888')] [2023-02-22 21:25:08,168][32245] Updated weights for policy 0, policy_version 720 (0.0008) [2023-02-22 21:25:09,866][32245] Updated weights for policy 0, policy_version 730 (0.0009) [2023-02-22 21:25:11,496][32245] Updated weights for policy 0, policy_version 740 (0.0008) [2023-02-22 21:25:13,140][32245] Updated weights for policy 0, policy_version 750 (0.0007) [2023-02-22 21:25:13,156][24717] Fps is (10 sec: 24166.4, 60 sec: 23893.3, 300 sec: 22755.6). Total num frames: 3072000. Throughput: 0: 5921.3. Samples: 752384. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-02-22 21:25:13,157][24717] Avg episode reward: [(0, '22.374')] [2023-02-22 21:25:14,875][32245] Updated weights for policy 0, policy_version 760 (0.0008) [2023-02-22 21:25:16,543][32245] Updated weights for policy 0, policy_version 770 (0.0007) [2023-02-22 21:25:18,156][24717] Fps is (10 sec: 24576.0, 60 sec: 23893.3, 300 sec: 22791.3). Total num frames: 3190784. Throughput: 0: 5945.4. Samples: 789058. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-02-22 21:25:18,157][24717] Avg episode reward: [(0, '22.226')] [2023-02-22 21:25:18,258][32245] Updated weights for policy 0, policy_version 780 (0.0006) [2023-02-22 21:25:19,995][32245] Updated weights for policy 0, policy_version 790 (0.0007) [2023-02-22 21:25:21,683][32245] Updated weights for policy 0, policy_version 800 (0.0006) [2023-02-22 21:25:23,156][24717] Fps is (10 sec: 23756.7, 60 sec: 23825.0, 300 sec: 22824.6). Total num frames: 3309568. Throughput: 0: 5944.3. Samples: 825322. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-02-22 21:25:23,158][24717] Avg episode reward: [(0, '23.568')] [2023-02-22 21:25:23,159][32230] Saving new best policy, reward=23.568! [2023-02-22 21:25:23,385][32245] Updated weights for policy 0, policy_version 810 (0.0007) [2023-02-22 21:25:25,018][32245] Updated weights for policy 0, policy_version 820 (0.0006) [2023-02-22 21:25:26,705][32245] Updated weights for policy 0, policy_version 830 (0.0006) [2023-02-22 21:25:28,156][24717] Fps is (10 sec: 24166.4, 60 sec: 23825.2, 300 sec: 22883.0). Total num frames: 3432448. Throughput: 0: 5950.8. Samples: 843522. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2023-02-22 21:25:28,157][24717] Avg episode reward: [(0, '21.152')] [2023-02-22 21:25:28,392][32245] Updated weights for policy 0, policy_version 840 (0.0008) [2023-02-22 21:25:30,067][32245] Updated weights for policy 0, policy_version 850 (0.0006) [2023-02-22 21:25:31,809][32245] Updated weights for policy 0, policy_version 860 (0.0009) [2023-02-22 21:25:33,156][24717] Fps is (10 sec: 24576.2, 60 sec: 23893.4, 300 sec: 22937.6). Total num frames: 3555328. Throughput: 0: 5961.2. Samples: 879730. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2023-02-22 21:25:33,157][24717] Avg episode reward: [(0, '25.351')] [2023-02-22 21:25:33,159][32230] Saving new best policy, reward=25.351! [2023-02-22 21:25:33,498][32245] Updated weights for policy 0, policy_version 870 (0.0006) [2023-02-22 21:25:35,212][32245] Updated weights for policy 0, policy_version 880 (0.0007) [2023-02-22 21:25:37,001][32245] Updated weights for policy 0, policy_version 890 (0.0012) [2023-02-22 21:25:38,156][24717] Fps is (10 sec: 23756.8, 60 sec: 23825.1, 300 sec: 22937.6). Total num frames: 3670016. Throughput: 0: 5960.4. Samples: 914910. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2023-02-22 21:25:38,158][24717] Avg episode reward: [(0, '24.882')] [2023-02-22 21:25:38,807][32245] Updated weights for policy 0, policy_version 900 (0.0010) [2023-02-22 21:25:40,496][32245] Updated weights for policy 0, policy_version 910 (0.0006) [2023-02-22 21:25:42,288][32245] Updated weights for policy 0, policy_version 920 (0.0008) [2023-02-22 21:25:43,157][24717] Fps is (10 sec: 22935.2, 60 sec: 23688.1, 300 sec: 22937.5). Total num frames: 3784704. Throughput: 0: 5959.6. Samples: 932726. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-02-22 21:25:43,158][24717] Avg episode reward: [(0, '23.635')] [2023-02-22 21:25:44,045][32245] Updated weights for policy 0, policy_version 930 (0.0008) [2023-02-22 21:25:45,787][32245] Updated weights for policy 0, policy_version 940 (0.0008) [2023-02-22 21:25:47,486][32245] Updated weights for policy 0, policy_version 950 (0.0007) [2023-02-22 21:25:48,156][24717] Fps is (10 sec: 23347.0, 60 sec: 23756.8, 300 sec: 22961.7). Total num frames: 3903488. Throughput: 0: 5975.9. Samples: 967920. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2023-02-22 21:25:48,158][24717] Avg episode reward: [(0, '25.987')] [2023-02-22 21:25:48,180][32230] Saving new best policy, reward=25.987! [2023-02-22 21:25:49,224][32245] Updated weights for policy 0, policy_version 960 (0.0008) [2023-02-22 21:25:50,941][32245] Updated weights for policy 0, policy_version 970 (0.0008) [2023-02-22 21:25:52,319][32230] Stopping Batcher_0... [2023-02-22 21:25:52,319][32230] Saving /home/flahoud/studies/collab/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... [2023-02-22 21:25:52,319][24717] Component Batcher_0 stopped! [2023-02-22 21:25:52,319][32230] Loop batcher_evt_loop terminating... [2023-02-22 21:25:52,333][24717] Component RolloutWorker_w5 stopped! [2023-02-22 21:25:52,334][32253] Stopping RolloutWorker_w4... [2023-02-22 21:25:52,335][32253] Loop rollout_proc4_evt_loop terminating... [2023-02-22 21:25:52,335][24717] Component RolloutWorker_w4 stopped! [2023-02-22 21:25:52,335][32252] Stopping RolloutWorker_w5... [2023-02-22 21:25:52,336][32252] Loop rollout_proc5_evt_loop terminating... [2023-02-22 21:25:52,337][32248] Stopping RolloutWorker_w2... [2023-02-22 21:25:52,337][32248] Loop rollout_proc2_evt_loop terminating... [2023-02-22 21:25:52,337][24717] Component RolloutWorker_w2 stopped! [2023-02-22 21:25:52,338][32247] Stopping RolloutWorker_w1... [2023-02-22 21:25:52,338][24717] Component RolloutWorker_w1 stopped! [2023-02-22 21:25:52,341][32262] Stopping RolloutWorker_w6... [2023-02-22 21:25:52,341][24717] Component RolloutWorker_w6 stopped! [2023-02-22 21:25:52,342][32262] Loop rollout_proc6_evt_loop terminating... [2023-02-22 21:25:52,339][32247] Loop rollout_proc1_evt_loop terminating... [2023-02-22 21:25:52,344][32245] Weights refcount: 2 0 [2023-02-22 21:25:52,345][32245] Stopping InferenceWorker_p0-w0... [2023-02-22 21:25:52,345][32245] Loop inference_proc0-0_evt_loop terminating... [2023-02-22 21:25:52,345][24717] Component InferenceWorker_p0-w0 stopped! [2023-02-22 21:25:52,348][24717] Component RolloutWorker_w0 stopped! [2023-02-22 21:25:52,348][32246] Stopping RolloutWorker_w0... [2023-02-22 21:25:52,349][32246] Loop rollout_proc0_evt_loop terminating... [2023-02-22 21:25:52,353][32263] Stopping RolloutWorker_w7... [2023-02-22 21:25:52,353][32263] Loop rollout_proc7_evt_loop terminating... [2023-02-22 21:25:52,353][24717] Component RolloutWorker_w7 stopped! [2023-02-22 21:25:52,380][32230] Saving new best policy, reward=28.129! [2023-02-22 21:25:52,411][32249] Stopping RolloutWorker_w3... [2023-02-22 21:25:52,411][32249] Loop rollout_proc3_evt_loop terminating... [2023-02-22 21:25:52,411][24717] Component RolloutWorker_w3 stopped! [2023-02-22 21:25:52,447][32230] Saving /home/flahoud/studies/collab/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... [2023-02-22 21:25:52,520][32230] Stopping LearnerWorker_p0... [2023-02-22 21:25:52,521][32230] Loop learner_proc0_evt_loop terminating... [2023-02-22 21:25:52,520][24717] Component LearnerWorker_p0 stopped! [2023-02-22 21:25:52,522][24717] Waiting for process learner_proc0 to stop... [2023-02-22 21:25:53,288][24717] Waiting for process inference_proc0-0 to join... [2023-02-22 21:25:53,289][24717] Waiting for process rollout_proc0 to join... [2023-02-22 21:25:53,290][24717] Waiting for process rollout_proc1 to join... [2023-02-22 21:25:53,290][24717] Waiting for process rollout_proc2 to join... [2023-02-22 21:25:53,291][24717] Waiting for process rollout_proc3 to join... [2023-02-22 21:25:53,292][24717] Waiting for process rollout_proc4 to join... [2023-02-22 21:25:53,293][24717] Waiting for process rollout_proc5 to join... [2023-02-22 21:25:53,293][24717] Waiting for process rollout_proc6 to join... [2023-02-22 21:25:53,294][24717] Waiting for process rollout_proc7 to join... [2023-02-22 21:25:53,295][24717] Batcher 0 profile tree view: batching: 13.1577, releasing_batches: 0.0181 [2023-02-22 21:25:53,295][24717] InferenceWorker_p0-w0 profile tree view: wait_policy: 0.0000 wait_policy_total: 4.3703 update_model: 2.3692 weight_update: 0.0008 one_step: 0.0019 handle_policy_step: 155.6174 deserialize: 7.8731, stack: 0.8396, obs_to_device_normalize: 39.5270, forward: 65.6351, send_messages: 13.4852 prepare_outputs: 21.2746 to_cpu: 12.7880 [2023-02-22 21:25:53,296][24717] Learner 0 profile tree view: misc: 0.0051, prepare_batch: 6.8617 train: 17.8979 epoch_init: 0.0044, minibatch_init: 0.0044, losses_postprocess: 0.2348, kl_divergence: 0.2579, after_optimizer: 3.0676 calculate_losses: 7.3319 losses_init: 0.0024, forward_head: 0.7714, bptt_initial: 4.3619, tail: 0.4261, advantages_returns: 0.1152, losses: 0.7021 bptt: 0.8259 bptt_forward_core: 0.7938 update: 6.7190 clip: 0.8093 [2023-02-22 21:25:53,297][24717] RolloutWorker_w0 profile tree view: wait_for_trajectories: 0.0989, enqueue_policy_requests: 5.3262, env_step: 71.2475, overhead: 6.5361, complete_rollouts: 0.5137 save_policy_outputs: 5.6181 split_output_tensors: 2.7734 [2023-02-22 21:25:53,297][24717] RolloutWorker_w7 profile tree view: wait_for_trajectories: 0.1168, enqueue_policy_requests: 5.4305, env_step: 72.9827, overhead: 6.5238, complete_rollouts: 0.4607 save_policy_outputs: 5.7456 split_output_tensors: 2.8446 [2023-02-22 21:25:53,299][24717] Loop Runner_EvtLoop terminating... [2023-02-22 21:25:53,300][24717] Runner profile tree view: main_loop: 182.3930 [2023-02-22 21:25:53,301][24717] Collected {0: 4005888}, FPS: 21962.9 [2023-02-22 21:26:34,224][24717] Loading existing experiment configuration from /home/flahoud/studies/collab/train_dir/default_experiment/config.json [2023-02-22 21:26:34,225][24717] Overriding arg 'num_workers' with value 1 passed from command line [2023-02-22 21:26:34,226][24717] Adding new argument 'no_render'=True that is not in the saved config file! [2023-02-22 21:26:34,226][24717] Adding new argument 'save_video'=True that is not in the saved config file! [2023-02-22 21:26:34,227][24717] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! [2023-02-22 21:26:34,228][24717] Adding new argument 'video_name'=None that is not in the saved config file! [2023-02-22 21:26:34,228][24717] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file! [2023-02-22 21:26:34,229][24717] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! [2023-02-22 21:26:34,229][24717] Adding new argument 'push_to_hub'=False that is not in the saved config file! [2023-02-22 21:26:34,230][24717] Adding new argument 'hf_repository'=None that is not in the saved config file! [2023-02-22 21:26:34,230][24717] Adding new argument 'policy_index'=0 that is not in the saved config file! [2023-02-22 21:26:34,231][24717] Adding new argument 'eval_deterministic'=False that is not in the saved config file! [2023-02-22 21:26:34,231][24717] Adding new argument 'train_script'=None that is not in the saved config file! [2023-02-22 21:26:34,232][24717] Adding new argument 'enjoy_script'=None that is not in the saved config file! [2023-02-22 21:26:34,233][24717] Using frameskip 1 and render_action_repeat=4 for evaluation [2023-02-22 21:26:34,249][24717] Doom resolution: 160x120, resize resolution: (128, 72) [2023-02-22 21:26:34,251][24717] RunningMeanStd input shape: (3, 72, 128) [2023-02-22 21:26:34,252][24717] RunningMeanStd input shape: (1,) [2023-02-22 21:26:34,264][24717] ConvEncoder: input_channels=3 [2023-02-22 21:26:34,367][24717] Conv encoder output size: 512 [2023-02-22 21:26:34,369][24717] Policy head output size: 512 [2023-02-22 21:26:37,138][24717] Loading state from checkpoint /home/flahoud/studies/collab/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... [2023-02-22 21:26:38,361][24717] Num frames 100... [2023-02-22 21:26:38,459][24717] Num frames 200... [2023-02-22 21:26:38,556][24717] Num frames 300... [2023-02-22 21:26:38,658][24717] Num frames 400... [2023-02-22 21:26:38,753][24717] Num frames 500... [2023-02-22 21:26:38,849][24717] Num frames 600... [2023-02-22 21:26:38,945][24717] Num frames 700... [2023-02-22 21:26:39,045][24717] Num frames 800... [2023-02-22 21:26:39,097][24717] Avg episode rewards: #0: 16.000, true rewards: #0: 8.000 [2023-02-22 21:26:39,098][24717] Avg episode reward: 16.000, avg true_objective: 8.000 [2023-02-22 21:26:39,202][24717] Num frames 900... [2023-02-22 21:26:39,299][24717] Num frames 1000... [2023-02-22 21:26:39,395][24717] Num frames 1100... [2023-02-22 21:26:39,491][24717] Num frames 1200... [2023-02-22 21:26:39,588][24717] Num frames 1300... [2023-02-22 21:26:39,690][24717] Num frames 1400... [2023-02-22 21:26:39,803][24717] Avg episode rewards: #0: 14.805, true rewards: #0: 7.305 [2023-02-22 21:26:39,804][24717] Avg episode reward: 14.805, avg true_objective: 7.305 [2023-02-22 21:26:39,843][24717] Num frames 1500... [2023-02-22 21:26:39,946][24717] Num frames 1600... [2023-02-22 21:26:40,046][24717] Num frames 1700... [2023-02-22 21:26:40,148][24717] Num frames 1800... [2023-02-22 21:26:40,247][24717] Num frames 1900... [2023-02-22 21:26:40,346][24717] Num frames 2000... [2023-02-22 21:26:40,445][24717] Num frames 2100... [2023-02-22 21:26:40,542][24717] Num frames 2200... [2023-02-22 21:26:40,689][24717] Avg episode rewards: #0: 14.977, true rewards: #0: 7.643 [2023-02-22 21:26:40,690][24717] Avg episode reward: 14.977, avg true_objective: 7.643 [2023-02-22 21:26:40,698][24717] Num frames 2300... [2023-02-22 21:26:40,798][24717] Num frames 2400... [2023-02-22 21:26:40,901][24717] Num frames 2500... [2023-02-22 21:26:40,999][24717] Num frames 2600... [2023-02-22 21:26:41,095][24717] Num frames 2700... [2023-02-22 21:26:41,199][24717] Num frames 2800... [2023-02-22 21:26:41,293][24717] Num frames 2900... [2023-02-22 21:26:41,384][24717] Num frames 3000... [2023-02-22 21:26:41,475][24717] Num frames 3100... [2023-02-22 21:26:41,570][24717] Num frames 3200... [2023-02-22 21:26:41,668][24717] Num frames 3300... [2023-02-22 21:26:41,772][24717] Num frames 3400... [2023-02-22 21:26:41,875][24717] Num frames 3500... [2023-02-22 21:26:42,000][24717] Avg episode rewards: #0: 18.183, true rewards: #0: 8.932 [2023-02-22 21:26:42,001][24717] Avg episode reward: 18.183, avg true_objective: 8.932 [2023-02-22 21:26:42,034][24717] Num frames 3600... [2023-02-22 21:26:42,140][24717] Num frames 3700... [2023-02-22 21:26:42,243][24717] Num frames 3800... [2023-02-22 21:26:42,342][24717] Num frames 3900... [2023-02-22 21:26:42,446][24717] Num frames 4000... [2023-02-22 21:26:42,548][24717] Num frames 4100... [2023-02-22 21:26:42,648][24717] Num frames 4200... [2023-02-22 21:26:42,776][24717] Avg episode rewards: #0: 16.954, true rewards: #0: 8.554 [2023-02-22 21:26:42,777][24717] Avg episode reward: 16.954, avg true_objective: 8.554 [2023-02-22 21:26:42,800][24717] Num frames 4300... [2023-02-22 21:26:42,898][24717] Num frames 4400... [2023-02-22 21:26:42,995][24717] Num frames 4500... [2023-02-22 21:26:43,090][24717] Num frames 4600... [2023-02-22 21:26:43,194][24717] Num frames 4700... [2023-02-22 21:26:43,294][24717] Num frames 4800... [2023-02-22 21:26:43,391][24717] Num frames 4900... [2023-02-22 21:26:43,491][24717] Num frames 5000... [2023-02-22 21:26:43,590][24717] Num frames 5100... [2023-02-22 21:26:43,687][24717] Num frames 5200... [2023-02-22 21:26:43,789][24717] Num frames 5300... [2023-02-22 21:26:43,889][24717] Num frames 5400... [2023-02-22 21:26:43,992][24717] Num frames 5500... [2023-02-22 21:26:44,093][24717] Num frames 5600... [2023-02-22 21:26:44,171][24717] Avg episode rewards: #0: 19.535, true rewards: #0: 9.368 [2023-02-22 21:26:44,172][24717] Avg episode reward: 19.535, avg true_objective: 9.368 [2023-02-22 21:26:44,254][24717] Num frames 5700... [2023-02-22 21:26:44,359][24717] Num frames 5800... [2023-02-22 21:26:44,461][24717] Num frames 5900... [2023-02-22 21:26:44,563][24717] Num frames 6000... [2023-02-22 21:26:44,663][24717] Num frames 6100... [2023-02-22 21:26:44,815][24717] Avg episode rewards: #0: 18.139, true rewards: #0: 8.853 [2023-02-22 21:26:44,817][24717] Avg episode reward: 18.139, avg true_objective: 8.853 [2023-02-22 21:26:44,820][24717] Num frames 6200... [2023-02-22 21:26:44,921][24717] Num frames 6300... [2023-02-22 21:26:45,024][24717] Num frames 6400... [2023-02-22 21:26:45,122][24717] Num frames 6500... [2023-02-22 21:26:45,222][24717] Num frames 6600... [2023-02-22 21:26:45,317][24717] Num frames 6700... [2023-02-22 21:26:45,413][24717] Num frames 6800... [2023-02-22 21:26:45,510][24717] Num frames 6900... [2023-02-22 21:26:45,604][24717] Num frames 7000... [2023-02-22 21:26:45,703][24717] Num frames 7100... [2023-02-22 21:26:45,813][24717] Num frames 7200... [2023-02-22 21:26:45,923][24717] Avg episode rewards: #0: 18.571, true rewards: #0: 9.071 [2023-02-22 21:26:45,924][24717] Avg episode reward: 18.571, avg true_objective: 9.071 [2023-02-22 21:26:45,967][24717] Num frames 7300... [2023-02-22 21:26:46,066][24717] Num frames 7400... [2023-02-22 21:26:46,160][24717] Num frames 7500... [2023-02-22 21:26:46,259][24717] Num frames 7600... [2023-02-22 21:26:46,352][24717] Num frames 7700... [2023-02-22 21:26:46,451][24717] Num frames 7800... [2023-02-22 21:26:46,548][24717] Num frames 7900... [2023-02-22 21:26:46,646][24717] Num frames 8000... [2023-02-22 21:26:46,726][24717] Avg episode rewards: #0: 18.250, true rewards: #0: 8.917 [2023-02-22 21:26:46,727][24717] Avg episode reward: 18.250, avg true_objective: 8.917 [2023-02-22 21:26:46,803][24717] Num frames 8100... [2023-02-22 21:26:46,897][24717] Num frames 8200... [2023-02-22 21:26:46,991][24717] Num frames 8300... [2023-02-22 21:26:47,084][24717] Num frames 8400... [2023-02-22 21:26:47,177][24717] Num frames 8500... [2023-02-22 21:26:47,270][24717] Num frames 8600... [2023-02-22 21:26:47,383][24717] Avg episode rewards: #0: 17.462, true rewards: #0: 8.662 [2023-02-22 21:26:47,384][24717] Avg episode reward: 17.462, avg true_objective: 8.662 [2023-02-22 21:27:02,989][24717] Replay video saved to /home/flahoud/studies/collab/train_dir/default_experiment/replay.mp4! [2023-02-22 21:32:31,282][24717] Loading existing experiment configuration from /home/flahoud/studies/collab/train_dir/default_experiment/config.json [2023-02-22 21:32:31,283][24717] Overriding arg 'num_workers' with value 1 passed from command line [2023-02-22 21:32:31,284][24717] Adding new argument 'no_render'=True that is not in the saved config file! [2023-02-22 21:32:31,285][24717] Adding new argument 'save_video'=True that is not in the saved config file! [2023-02-22 21:32:31,286][24717] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! [2023-02-22 21:32:31,286][24717] Adding new argument 'video_name'=None that is not in the saved config file! [2023-02-22 21:32:31,286][24717] Adding new argument 'max_num_frames'=100000 that is not in the saved config file! [2023-02-22 21:32:31,287][24717] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! [2023-02-22 21:32:31,287][24717] Adding new argument 'push_to_hub'=True that is not in the saved config file! [2023-02-22 21:32:31,288][24717] Adding new argument 'hf_repository'='GrimReaperSam/rl_course_vizdoom_health_gathering_supreme' that is not in the saved config file! [2023-02-22 21:32:31,288][24717] Adding new argument 'policy_index'=0 that is not in the saved config file! [2023-02-22 21:32:31,289][24717] Adding new argument 'eval_deterministic'=False that is not in the saved config file! [2023-02-22 21:32:31,289][24717] Adding new argument 'train_script'=None that is not in the saved config file! [2023-02-22 21:32:31,291][24717] Adding new argument 'enjoy_script'=None that is not in the saved config file! [2023-02-22 21:32:31,292][24717] Using frameskip 1 and render_action_repeat=4 for evaluation [2023-02-22 21:32:31,303][24717] RunningMeanStd input shape: (3, 72, 128) [2023-02-22 21:32:31,304][24717] RunningMeanStd input shape: (1,) [2023-02-22 21:32:31,315][24717] ConvEncoder: input_channels=3 [2023-02-22 21:32:31,371][24717] Conv encoder output size: 512 [2023-02-22 21:32:31,372][24717] Policy head output size: 512 [2023-02-22 21:32:31,403][24717] Loading state from checkpoint /home/flahoud/studies/collab/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... [2023-02-22 21:32:31,890][24717] Num frames 100... [2023-02-22 21:32:31,981][24717] Num frames 200... [2023-02-22 21:32:32,072][24717] Num frames 300... [2023-02-22 21:32:32,172][24717] Num frames 400... [2023-02-22 21:32:32,320][24717] Avg episode rewards: #0: 7.980, true rewards: #0: 4.980 [2023-02-22 21:32:32,321][24717] Avg episode reward: 7.980, avg true_objective: 4.980 [2023-02-22 21:32:32,323][24717] Num frames 500... [2023-02-22 21:32:32,420][24717] Num frames 600... [2023-02-22 21:32:32,518][24717] Num frames 700... [2023-02-22 21:32:32,623][24717] Num frames 800... [2023-02-22 21:32:32,718][24717] Num frames 900... [2023-02-22 21:32:32,811][24717] Num frames 1000... [2023-02-22 21:32:32,928][24717] Num frames 1100... [2023-02-22 21:32:33,028][24717] Num frames 1200... [2023-02-22 21:32:33,125][24717] Num frames 1300... [2023-02-22 21:32:33,222][24717] Num frames 1400... [2023-02-22 21:32:33,324][24717] Num frames 1500... [2023-02-22 21:32:33,427][24717] Num frames 1600... [2023-02-22 21:32:33,531][24717] Num frames 1700... [2023-02-22 21:32:33,631][24717] Num frames 1800... [2023-02-22 21:32:33,730][24717] Num frames 1900... [2023-02-22 21:32:33,835][24717] Num frames 2000... [2023-02-22 21:32:33,937][24717] Num frames 2100... [2023-02-22 21:32:34,048][24717] Num frames 2200... [2023-02-22 21:32:34,193][24717] Avg episode rewards: #0: 26.950, true rewards: #0: 11.450 [2023-02-22 21:32:34,194][24717] Avg episode reward: 26.950, avg true_objective: 11.450 [2023-02-22 21:32:34,206][24717] Num frames 2300... [2023-02-22 21:32:34,315][24717] Num frames 2400... [2023-02-22 21:32:34,424][24717] Num frames 2500... [2023-02-22 21:32:34,528][24717] Num frames 2600... [2023-02-22 21:32:34,632][24717] Num frames 2700... [2023-02-22 21:32:34,736][24717] Num frames 2800... [2023-02-22 21:32:34,843][24717] Num frames 2900... [2023-02-22 21:32:34,943][24717] Num frames 3000... [2023-02-22 21:32:35,049][24717] Num frames 3100... [2023-02-22 21:32:35,153][24717] Num frames 3200... [2023-02-22 21:32:35,254][24717] Num frames 3300... [2023-02-22 21:32:35,356][24717] Num frames 3400... [2023-02-22 21:32:35,457][24717] Num frames 3500... [2023-02-22 21:32:35,558][24717] Num frames 3600... [2023-02-22 21:32:35,655][24717] Num frames 3700... [2023-02-22 21:32:35,741][24717] Avg episode rewards: #0: 30.766, true rewards: #0: 12.433 [2023-02-22 21:32:35,742][24717] Avg episode reward: 30.766, avg true_objective: 12.433 [2023-02-22 21:32:35,816][24717] Num frames 3800... [2023-02-22 21:32:35,916][24717] Num frames 3900... [2023-02-22 21:32:36,017][24717] Num frames 4000... [2023-02-22 21:32:36,125][24717] Num frames 4100... [2023-02-22 21:32:36,232][24717] Num frames 4200... [2023-02-22 21:32:36,342][24717] Num frames 4300... [2023-02-22 21:32:36,445][24717] Num frames 4400... [2023-02-22 21:32:36,553][24717] Num frames 4500... [2023-02-22 21:32:36,664][24717] Num frames 4600... [2023-02-22 21:32:36,734][24717] Avg episode rewards: #0: 28.032, true rewards: #0: 11.532 [2023-02-22 21:32:36,735][24717] Avg episode reward: 28.032, avg true_objective: 11.532 [2023-02-22 21:32:36,823][24717] Num frames 4700... [2023-02-22 21:32:36,929][24717] Num frames 4800... [2023-02-22 21:32:37,027][24717] Num frames 4900... [2023-02-22 21:32:37,131][24717] Num frames 5000... [2023-02-22 21:32:37,231][24717] Num frames 5100... [2023-02-22 21:32:37,331][24717] Num frames 5200... [2023-02-22 21:32:37,428][24717] Num frames 5300... [2023-02-22 21:32:37,530][24717] Num frames 5400... [2023-02-22 21:32:37,643][24717] Num frames 5500... [2023-02-22 21:32:37,747][24717] Num frames 5600... [2023-02-22 21:32:37,817][24717] Avg episode rewards: #0: 27.428, true rewards: #0: 11.228 [2023-02-22 21:32:37,818][24717] Avg episode reward: 27.428, avg true_objective: 11.228 [2023-02-22 21:32:37,907][24717] Num frames 5700... [2023-02-22 21:32:38,007][24717] Num frames 5800... [2023-02-22 21:32:38,106][24717] Num frames 5900... [2023-02-22 21:32:38,202][24717] Num frames 6000... [2023-02-22 21:32:38,296][24717] Num frames 6100... [2023-02-22 21:32:38,398][24717] Num frames 6200... [2023-02-22 21:32:38,494][24717] Num frames 6300... [2023-02-22 21:32:38,593][24717] Num frames 6400... [2023-02-22 21:32:38,689][24717] Num frames 6500... [2023-02-22 21:32:38,755][24717] Avg episode rewards: #0: 25.850, true rewards: #0: 10.850 [2023-02-22 21:32:38,757][24717] Avg episode reward: 25.850, avg true_objective: 10.850 [2023-02-22 21:32:38,845][24717] Num frames 6600... [2023-02-22 21:32:38,943][24717] Num frames 6700... [2023-02-22 21:32:39,043][24717] Num frames 6800... [2023-02-22 21:32:39,187][24717] Avg episode rewards: #0: 23.134, true rewards: #0: 9.849 [2023-02-22 21:32:39,188][24717] Avg episode reward: 23.134, avg true_objective: 9.849 [2023-02-22 21:32:39,195][24717] Num frames 6900... [2023-02-22 21:32:39,302][24717] Num frames 7000... [2023-02-22 21:32:39,399][24717] Num frames 7100... [2023-02-22 21:32:39,501][24717] Num frames 7200... [2023-02-22 21:32:39,600][24717] Num frames 7300... [2023-02-22 21:32:39,696][24717] Num frames 7400... [2023-02-22 21:32:39,790][24717] Num frames 7500... [2023-02-22 21:32:39,884][24717] Num frames 7600... [2023-02-22 21:32:39,985][24717] Num frames 7700... [2023-02-22 21:32:40,084][24717] Num frames 7800... [2023-02-22 21:32:40,187][24717] Num frames 7900... [2023-02-22 21:32:40,292][24717] Avg episode rewards: #0: 22.937, true rewards: #0: 9.937 [2023-02-22 21:32:40,294][24717] Avg episode reward: 22.937, avg true_objective: 9.937 [2023-02-22 21:32:40,349][24717] Num frames 8000... [2023-02-22 21:32:40,445][24717] Num frames 8100... [2023-02-22 21:32:40,550][24717] Num frames 8200... [2023-02-22 21:32:40,657][24717] Num frames 8300... [2023-02-22 21:32:40,805][24717] Avg episode rewards: #0: 20.998, true rewards: #0: 9.331 [2023-02-22 21:32:40,806][24717] Avg episode reward: 20.998, avg true_objective: 9.331 [2023-02-22 21:32:40,809][24717] Num frames 8400... [2023-02-22 21:32:40,907][24717] Num frames 8500... [2023-02-22 21:32:41,012][24717] Num frames 8600... [2023-02-22 21:32:41,114][24717] Num frames 8700... [2023-02-22 21:32:41,255][24717] Avg episode rewards: #0: 19.282, true rewards: #0: 8.782 [2023-02-22 21:32:41,256][24717] Avg episode reward: 19.282, avg true_objective: 8.782 [2023-02-22 21:32:57,417][24717] Replay video saved to /home/flahoud/studies/collab/train_dir/default_experiment/replay.mp4!