[2024-08-04 13:11:06,292][00695] Saving configuration to /content/train_dir/default_experiment/config.json... [2024-08-04 13:11:06,294][00695] Rollout worker 0 uses device cpu [2024-08-04 13:11:06,295][00695] Rollout worker 1 uses device cpu [2024-08-04 13:11:06,296][00695] Rollout worker 2 uses device cpu [2024-08-04 13:11:06,297][00695] Rollout worker 3 uses device cpu [2024-08-04 13:11:06,300][00695] Rollout worker 4 uses device cpu [2024-08-04 13:11:06,301][00695] Rollout worker 5 uses device cpu [2024-08-04 13:11:06,302][00695] Rollout worker 6 uses device cpu [2024-08-04 13:11:06,305][00695] Rollout worker 7 uses device cpu [2024-08-04 13:11:06,398][00695] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2024-08-04 13:11:06,399][00695] InferenceWorker_p0-w0: min num requests: 2 [2024-08-04 13:11:06,432][00695] Starting all processes... [2024-08-04 13:11:06,433][00695] Starting process learner_proc0 [2024-08-04 13:11:07,608][00695] Starting all processes... [2024-08-04 13:11:07,614][00695] Starting process inference_proc0-0 [2024-08-04 13:11:07,616][00695] Starting process rollout_proc0 [2024-08-04 13:11:07,616][00695] Starting process rollout_proc1 [2024-08-04 13:11:07,622][00695] Starting process rollout_proc2 [2024-08-04 13:11:07,625][00695] Starting process rollout_proc3 [2024-08-04 13:11:07,626][00695] Starting process rollout_proc4 [2024-08-04 13:11:07,627][00695] Starting process rollout_proc5 [2024-08-04 13:11:07,628][00695] Starting process rollout_proc6 [2024-08-04 13:11:07,634][00695] Starting process rollout_proc7 [2024-08-04 13:11:10,181][01571] Worker 5 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] [2024-08-04 13:11:10,196][01567] Worker 1 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] [2024-08-04 13:11:10,305][01584] Worker 7 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] [2024-08-04 13:11:10,350][01569] Worker 3 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] [2024-08-04 13:11:10,407][01566] Worker 0 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] [2024-08-04 13:11:10,440][01572] Worker 6 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] [2024-08-04 13:11:10,475][01552] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2024-08-04 13:11:10,475][01552] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0 [2024-08-04 13:11:10,490][01552] Num visible devices: 1 [2024-08-04 13:11:10,504][01552] Starting seed is not provided [2024-08-04 13:11:10,504][01552] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2024-08-04 13:11:10,504][01552] Initializing actor-critic model on device cuda:0 [2024-08-04 13:11:10,505][01552] RunningMeanStd input shape: (3, 72, 128) [2024-08-04 13:11:10,507][01552] RunningMeanStd input shape: (1,) [2024-08-04 13:11:10,512][01568] Worker 2 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] [2024-08-04 13:11:10,521][01552] ConvEncoder: input_channels=3 [2024-08-04 13:11:10,532][01565] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2024-08-04 13:11:10,532][01565] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0 [2024-08-04 13:11:10,547][01565] Num visible devices: 1 [2024-08-04 13:11:10,590][01570] Worker 4 uses CPU cores [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] [2024-08-04 13:11:10,734][01552] Conv encoder output size: 512 [2024-08-04 13:11:10,734][01552] Policy head output size: 512 [2024-08-04 13:11:10,784][01552] Created Actor Critic model with architecture: [2024-08-04 13:11:10,784][01552] 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) ) ) [2024-08-04 13:11:10,979][01552] Using optimizer [2024-08-04 13:11:11,705][01552] No checkpoints found [2024-08-04 13:11:11,705][01552] Did not load from checkpoint, starting from scratch! [2024-08-04 13:11:11,706][01552] Initialized policy 0 weights for model version 0 [2024-08-04 13:11:11,708][01552] LearnerWorker_p0 finished initialization! [2024-08-04 13:11:11,708][01552] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2024-08-04 13:11:11,788][01565] RunningMeanStd input shape: (3, 72, 128) [2024-08-04 13:11:11,789][01565] RunningMeanStd input shape: (1,) [2024-08-04 13:11:11,801][01565] ConvEncoder: input_channels=3 [2024-08-04 13:11:11,909][01565] Conv encoder output size: 512 [2024-08-04 13:11:11,909][01565] Policy head output size: 512 [2024-08-04 13:11:11,963][00695] Inference worker 0-0 is ready! [2024-08-04 13:11:11,964][00695] All inference workers are ready! Signal rollout workers to start! [2024-08-04 13:11:11,997][01584] Doom resolution: 160x120, resize resolution: (128, 72) [2024-08-04 13:11:11,997][01569] Doom resolution: 160x120, resize resolution: (128, 72) [2024-08-04 13:11:12,016][01572] Doom resolution: 160x120, resize resolution: (128, 72) [2024-08-04 13:11:12,017][01570] Doom resolution: 160x120, resize resolution: (128, 72) [2024-08-04 13:11:12,018][01566] Doom resolution: 160x120, resize resolution: (128, 72) [2024-08-04 13:11:12,018][01568] Doom resolution: 160x120, resize resolution: (128, 72) [2024-08-04 13:11:12,018][01567] Doom resolution: 160x120, resize resolution: (128, 72) [2024-08-04 13:11:12,018][01571] Doom resolution: 160x120, resize resolution: (128, 72) [2024-08-04 13:11:12,306][01584] Decorrelating experience for 0 frames... [2024-08-04 13:11:12,306][01569] Decorrelating experience for 0 frames... [2024-08-04 13:11:12,324][01572] Decorrelating experience for 0 frames... [2024-08-04 13:11:12,325][01568] Decorrelating experience for 0 frames... [2024-08-04 13:11:12,326][01567] Decorrelating experience for 0 frames... [2024-08-04 13:11:12,332][00695] 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) [2024-08-04 13:11:12,333][01566] Decorrelating experience for 0 frames... [2024-08-04 13:11:12,547][01584] Decorrelating experience for 32 frames... [2024-08-04 13:11:12,550][01569] Decorrelating experience for 32 frames... [2024-08-04 13:11:12,568][01572] Decorrelating experience for 32 frames... [2024-08-04 13:11:12,568][01567] Decorrelating experience for 32 frames... [2024-08-04 13:11:12,614][01570] Decorrelating experience for 0 frames... [2024-08-04 13:11:12,652][01568] Decorrelating experience for 32 frames... [2024-08-04 13:11:12,809][01571] Decorrelating experience for 0 frames... [2024-08-04 13:11:12,852][01570] Decorrelating experience for 32 frames... [2024-08-04 13:11:12,897][01584] Decorrelating experience for 64 frames... [2024-08-04 13:11:12,920][01572] Decorrelating experience for 64 frames... [2024-08-04 13:11:12,955][01567] Decorrelating experience for 64 frames... [2024-08-04 13:11:13,064][01571] Decorrelating experience for 32 frames... [2024-08-04 13:11:13,073][01568] Decorrelating experience for 64 frames... [2024-08-04 13:11:13,148][01569] Decorrelating experience for 64 frames... [2024-08-04 13:11:13,210][01584] Decorrelating experience for 96 frames... [2024-08-04 13:11:13,227][01570] Decorrelating experience for 64 frames... [2024-08-04 13:11:13,323][01572] Decorrelating experience for 96 frames... [2024-08-04 13:11:13,354][01566] Decorrelating experience for 32 frames... [2024-08-04 13:11:13,402][01571] Decorrelating experience for 64 frames... [2024-08-04 13:11:13,436][01568] Decorrelating experience for 96 frames... [2024-08-04 13:11:13,500][01569] Decorrelating experience for 96 frames... [2024-08-04 13:11:13,538][01567] Decorrelating experience for 96 frames... [2024-08-04 13:11:13,631][01570] Decorrelating experience for 96 frames... [2024-08-04 13:11:13,768][01571] Decorrelating experience for 96 frames... [2024-08-04 13:11:13,776][01566] Decorrelating experience for 64 frames... [2024-08-04 13:11:14,053][01566] Decorrelating experience for 96 frames... [2024-08-04 13:11:14,922][01552] Signal inference workers to stop experience collection... [2024-08-04 13:11:14,926][01565] InferenceWorker_p0-w0: stopping experience collection [2024-08-04 13:11:17,161][01552] Signal inference workers to resume experience collection... [2024-08-04 13:11:17,162][01565] InferenceWorker_p0-w0: resuming experience collection [2024-08-04 13:11:17,332][00695] Fps is (10 sec: 819.2, 60 sec: 819.2, 300 sec: 819.2). Total num frames: 4096. Throughput: 0: 484.8. Samples: 2424. Policy #0 lag: (min: 0.0, avg: 0.0, max: 0.0) [2024-08-04 13:11:17,333][00695] Avg episode reward: [(0, '1.863')] [2024-08-04 13:11:19,216][01565] Updated weights for policy 0, policy_version 10 (0.0190) [2024-08-04 13:11:21,399][01565] Updated weights for policy 0, policy_version 20 (0.0013) [2024-08-04 13:11:22,332][00695] Fps is (10 sec: 9830.4, 60 sec: 9830.4, 300 sec: 9830.4). Total num frames: 98304. Throughput: 0: 1882.4. Samples: 18824. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) [2024-08-04 13:11:22,336][00695] Avg episode reward: [(0, '4.437')] [2024-08-04 13:11:23,551][01565] Updated weights for policy 0, policy_version 30 (0.0013) [2024-08-04 13:11:25,611][01565] Updated weights for policy 0, policy_version 40 (0.0012) [2024-08-04 13:11:26,393][00695] Heartbeat connected on LearnerWorker_p0 [2024-08-04 13:11:26,396][00695] Heartbeat connected on Batcher_0 [2024-08-04 13:11:26,405][00695] Heartbeat connected on RolloutWorker_w0 [2024-08-04 13:11:26,408][00695] Heartbeat connected on InferenceWorker_p0-w0 [2024-08-04 13:11:26,411][00695] Heartbeat connected on RolloutWorker_w1 [2024-08-04 13:11:26,414][00695] Heartbeat connected on RolloutWorker_w2 [2024-08-04 13:11:26,418][00695] Heartbeat connected on RolloutWorker_w3 [2024-08-04 13:11:26,420][00695] Heartbeat connected on RolloutWorker_w4 [2024-08-04 13:11:26,426][00695] Heartbeat connected on RolloutWorker_w5 [2024-08-04 13:11:26,428][00695] Heartbeat connected on RolloutWorker_w6 [2024-08-04 13:11:26,433][00695] Heartbeat connected on RolloutWorker_w7 [2024-08-04 13:11:27,332][00695] Fps is (10 sec: 19251.0, 60 sec: 13107.2, 300 sec: 13107.2). Total num frames: 196608. Throughput: 0: 3212.8. Samples: 48192. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) [2024-08-04 13:11:27,334][00695] Avg episode reward: [(0, '4.414')] [2024-08-04 13:11:27,337][01552] Saving new best policy, reward=4.414! [2024-08-04 13:11:27,684][01565] Updated weights for policy 0, policy_version 50 (0.0012) [2024-08-04 13:11:29,750][01565] Updated weights for policy 0, policy_version 60 (0.0012) [2024-08-04 13:11:31,816][01565] Updated weights for policy 0, policy_version 70 (0.0012) [2024-08-04 13:11:32,332][00695] Fps is (10 sec: 19660.8, 60 sec: 14745.6, 300 sec: 14745.6). Total num frames: 294912. Throughput: 0: 3152.7. Samples: 63054. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2024-08-04 13:11:32,334][00695] Avg episode reward: [(0, '4.503')] [2024-08-04 13:11:32,342][01552] Saving new best policy, reward=4.503! [2024-08-04 13:11:33,894][01565] Updated weights for policy 0, policy_version 80 (0.0013) [2024-08-04 13:11:36,055][01565] Updated weights for policy 0, policy_version 90 (0.0013) [2024-08-04 13:11:37,332][00695] Fps is (10 sec: 19251.3, 60 sec: 15564.8, 300 sec: 15564.8). Total num frames: 389120. Throughput: 0: 3697.1. Samples: 92428. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2024-08-04 13:11:37,334][00695] Avg episode reward: [(0, '4.490')] [2024-08-04 13:11:38,155][01565] Updated weights for policy 0, policy_version 100 (0.0012) [2024-08-04 13:11:40,197][01565] Updated weights for policy 0, policy_version 110 (0.0012) [2024-08-04 13:11:42,264][01565] Updated weights for policy 0, policy_version 120 (0.0013) [2024-08-04 13:11:42,332][00695] Fps is (10 sec: 19660.7, 60 sec: 16384.0, 300 sec: 16384.0). Total num frames: 491520. Throughput: 0: 4066.9. Samples: 122008. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2024-08-04 13:11:42,335][00695] Avg episode reward: [(0, '4.385')] [2024-08-04 13:11:44,339][01565] Updated weights for policy 0, policy_version 130 (0.0013) [2024-08-04 13:11:46,418][01565] Updated weights for policy 0, policy_version 140 (0.0012) [2024-08-04 13:11:47,332][00695] Fps is (10 sec: 20070.4, 60 sec: 16852.1, 300 sec: 16852.1). Total num frames: 589824. Throughput: 0: 3908.2. Samples: 136786. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2024-08-04 13:11:47,334][00695] Avg episode reward: [(0, '4.487')] [2024-08-04 13:11:48,535][01565] Updated weights for policy 0, policy_version 150 (0.0012) [2024-08-04 13:11:50,687][01565] Updated weights for policy 0, policy_version 160 (0.0013) [2024-08-04 13:11:52,332][00695] Fps is (10 sec: 19251.2, 60 sec: 17100.8, 300 sec: 17100.8). Total num frames: 684032. Throughput: 0: 4141.5. Samples: 165660. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2024-08-04 13:11:52,334][00695] Avg episode reward: [(0, '4.638')] [2024-08-04 13:11:52,372][01552] Saving new best policy, reward=4.638! [2024-08-04 13:11:52,794][01565] Updated weights for policy 0, policy_version 170 (0.0012) [2024-08-04 13:11:54,861][01565] Updated weights for policy 0, policy_version 180 (0.0012) [2024-08-04 13:11:56,919][01565] Updated weights for policy 0, policy_version 190 (0.0012) [2024-08-04 13:11:57,332][00695] Fps is (10 sec: 19660.8, 60 sec: 17476.3, 300 sec: 17476.3). Total num frames: 786432. Throughput: 0: 4342.6. Samples: 195418. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2024-08-04 13:11:57,334][00695] Avg episode reward: [(0, '4.570')] [2024-08-04 13:11:58,968][01565] Updated weights for policy 0, policy_version 200 (0.0012) [2024-08-04 13:12:01,026][01565] Updated weights for policy 0, policy_version 210 (0.0013) [2024-08-04 13:12:02,332][00695] Fps is (10 sec: 20070.5, 60 sec: 17694.7, 300 sec: 17694.7). Total num frames: 884736. Throughput: 0: 4620.6. Samples: 210350. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) [2024-08-04 13:12:02,335][00695] Avg episode reward: [(0, '4.711')] [2024-08-04 13:12:02,342][01552] Saving new best policy, reward=4.711! [2024-08-04 13:12:03,205][01565] Updated weights for policy 0, policy_version 220 (0.0013) [2024-08-04 13:12:05,363][01565] Updated weights for policy 0, policy_version 230 (0.0013) [2024-08-04 13:12:07,332][00695] Fps is (10 sec: 19251.3, 60 sec: 17799.0, 300 sec: 17799.0). Total num frames: 978944. Throughput: 0: 4896.9. Samples: 239184. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2024-08-04 13:12:07,334][00695] Avg episode reward: [(0, '4.390')] [2024-08-04 13:12:07,420][01565] Updated weights for policy 0, policy_version 240 (0.0012) [2024-08-04 13:12:09,477][01565] Updated weights for policy 0, policy_version 250 (0.0012) [2024-08-04 13:12:11,544][01565] Updated weights for policy 0, policy_version 260 (0.0012) [2024-08-04 13:12:12,332][00695] Fps is (10 sec: 19251.2, 60 sec: 17954.1, 300 sec: 17954.1). Total num frames: 1077248. Throughput: 0: 4906.6. Samples: 268990. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2024-08-04 13:12:12,334][00695] Avg episode reward: [(0, '4.775')] [2024-08-04 13:12:12,342][01552] Saving new best policy, reward=4.775! [2024-08-04 13:12:13,613][01565] Updated weights for policy 0, policy_version 270 (0.0012) [2024-08-04 13:12:15,727][01565] Updated weights for policy 0, policy_version 280 (0.0013) [2024-08-04 13:12:17,332][00695] Fps is (10 sec: 19660.7, 60 sec: 19524.3, 300 sec: 18085.4). Total num frames: 1175552. Throughput: 0: 4909.8. Samples: 283994. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2024-08-04 13:12:17,334][00695] Avg episode reward: [(0, '4.494')] [2024-08-04 13:12:17,978][01565] Updated weights for policy 0, policy_version 290 (0.0012) [2024-08-04 13:12:20,075][01565] Updated weights for policy 0, policy_version 300 (0.0013) [2024-08-04 13:12:22,137][01565] Updated weights for policy 0, policy_version 310 (0.0013) [2024-08-04 13:12:22,332][00695] Fps is (10 sec: 19251.2, 60 sec: 19524.3, 300 sec: 18139.4). Total num frames: 1269760. Throughput: 0: 4891.4. Samples: 312540. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2024-08-04 13:12:22,334][00695] Avg episode reward: [(0, '4.701')] [2024-08-04 13:12:24,196][01565] Updated weights for policy 0, policy_version 320 (0.0013) [2024-08-04 13:12:26,248][01565] Updated weights for policy 0, policy_version 330 (0.0013) [2024-08-04 13:12:27,332][00695] Fps is (10 sec: 19660.8, 60 sec: 19592.5, 300 sec: 18295.5). Total num frames: 1372160. Throughput: 0: 4896.2. Samples: 342336. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2024-08-04 13:12:27,335][00695] Avg episode reward: [(0, '4.511')] [2024-08-04 13:12:28,305][01565] Updated weights for policy 0, policy_version 340 (0.0013) [2024-08-04 13:12:30,411][01565] Updated weights for policy 0, policy_version 350 (0.0012) [2024-08-04 13:12:32,332][00695] Fps is (10 sec: 20070.4, 60 sec: 19592.5, 300 sec: 18380.8). Total num frames: 1470464. Throughput: 0: 4896.7. Samples: 357140. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2024-08-04 13:12:32,334][00695] Avg episode reward: [(0, '4.791')] [2024-08-04 13:12:32,341][01552] Saving new best policy, reward=4.791! [2024-08-04 13:12:32,546][01565] Updated weights for policy 0, policy_version 360 (0.0013) [2024-08-04 13:12:34,617][01565] Updated weights for policy 0, policy_version 370 (0.0012) [2024-08-04 13:12:36,699][01565] Updated weights for policy 0, policy_version 380 (0.0013) [2024-08-04 13:12:37,332][00695] Fps is (10 sec: 19251.1, 60 sec: 19592.5, 300 sec: 18407.9). Total num frames: 1564672. Throughput: 0: 4904.0. Samples: 386340. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2024-08-04 13:12:37,334][00695] Avg episode reward: [(0, '4.897')] [2024-08-04 13:12:37,337][01552] Saving new best policy, reward=4.897! [2024-08-04 13:12:38,801][01565] Updated weights for policy 0, policy_version 390 (0.0013) [2024-08-04 13:12:40,904][01565] Updated weights for policy 0, policy_version 400 (0.0012) [2024-08-04 13:12:42,332][00695] Fps is (10 sec: 19251.2, 60 sec: 19524.3, 300 sec: 18477.5). Total num frames: 1662976. Throughput: 0: 4893.1. Samples: 415606. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) [2024-08-04 13:12:42,334][00695] Avg episode reward: [(0, '4.707')] [2024-08-04 13:12:42,985][01565] Updated weights for policy 0, policy_version 410 (0.0013) [2024-08-04 13:12:45,197][01565] Updated weights for policy 0, policy_version 420 (0.0013) [2024-08-04 13:12:47,332][00695] Fps is (10 sec: 19251.2, 60 sec: 19456.0, 300 sec: 18496.7). Total num frames: 1757184. Throughput: 0: 4879.7. Samples: 429936. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2024-08-04 13:12:47,334][00695] Avg episode reward: [(0, '5.160')] [2024-08-04 13:12:47,350][01552] Saving new best policy, reward=5.160! [2024-08-04 13:12:47,351][01565] Updated weights for policy 0, policy_version 430 (0.0012) [2024-08-04 13:12:49,415][01565] Updated weights for policy 0, policy_version 440 (0.0012) [2024-08-04 13:12:51,496][01565] Updated weights for policy 0, policy_version 450 (0.0012) [2024-08-04 13:12:52,332][00695] Fps is (10 sec: 19660.8, 60 sec: 19592.6, 300 sec: 18595.8). Total num frames: 1859584. Throughput: 0: 4884.7. Samples: 458994. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2024-08-04 13:12:52,335][00695] Avg episode reward: [(0, '5.076')] [2024-08-04 13:12:53,581][01565] Updated weights for policy 0, policy_version 460 (0.0012) [2024-08-04 13:12:55,657][01565] Updated weights for policy 0, policy_version 470 (0.0012) [2024-08-04 13:12:57,332][00695] Fps is (10 sec: 20070.4, 60 sec: 19524.3, 300 sec: 18646.6). Total num frames: 1957888. Throughput: 0: 4877.9. Samples: 488496. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2024-08-04 13:12:57,335][00695] Avg episode reward: [(0, '5.323')] [2024-08-04 13:12:57,336][01552] Saving new best policy, reward=5.323! [2024-08-04 13:12:57,781][01565] Updated weights for policy 0, policy_version 480 (0.0013) [2024-08-04 13:12:59,930][01565] Updated weights for policy 0, policy_version 490 (0.0013) [2024-08-04 13:13:02,047][01565] Updated weights for policy 0, policy_version 500 (0.0013) [2024-08-04 13:13:02,332][00695] Fps is (10 sec: 19251.2, 60 sec: 19456.0, 300 sec: 18655.4). Total num frames: 2052096. Throughput: 0: 4859.5. Samples: 502674. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2024-08-04 13:13:02,336][00695] Avg episode reward: [(0, '5.068')] [2024-08-04 13:13:02,346][01552] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000501_2052096.pth... [2024-08-04 13:13:04,107][01565] Updated weights for policy 0, policy_version 510 (0.0013) [2024-08-04 13:13:06,166][01565] Updated weights for policy 0, policy_version 520 (0.0012) [2024-08-04 13:13:07,332][00695] Fps is (10 sec: 19251.2, 60 sec: 19524.2, 300 sec: 18699.1). Total num frames: 2150400. Throughput: 0: 4882.7. Samples: 532260. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2024-08-04 13:13:07,334][00695] Avg episode reward: [(0, '4.931')] [2024-08-04 13:13:08,227][01565] Updated weights for policy 0, policy_version 530 (0.0013) [2024-08-04 13:13:10,282][01565] Updated weights for policy 0, policy_version 540 (0.0012) [2024-08-04 13:13:12,332][00695] Fps is (10 sec: 19660.9, 60 sec: 19524.3, 300 sec: 18739.2). Total num frames: 2248704. Throughput: 0: 4882.3. Samples: 562040. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0) [2024-08-04 13:13:12,334][00695] Avg episode reward: [(0, '5.982')] [2024-08-04 13:13:12,364][01552] Saving new best policy, reward=5.982! [2024-08-04 13:13:12,365][01565] Updated weights for policy 0, policy_version 550 (0.0013) [2024-08-04 13:13:14,530][01565] Updated weights for policy 0, policy_version 560 (0.0013) [2024-08-04 13:13:16,605][01565] Updated weights for policy 0, policy_version 570 (0.0012) [2024-08-04 13:13:17,332][00695] Fps is (10 sec: 19660.8, 60 sec: 19524.2, 300 sec: 18776.1). Total num frames: 2347008. Throughput: 0: 4872.3. Samples: 576392. Policy #0 lag: (min: 0.0, avg: 0.9, max: 2.0) [2024-08-04 13:13:17,334][00695] Avg episode reward: [(0, '7.751')] [2024-08-04 13:13:17,337][01552] Saving new best policy, reward=7.751! [2024-08-04 13:13:18,702][01565] Updated weights for policy 0, policy_version 580 (0.0012) [2024-08-04 13:13:20,750][01565] Updated weights for policy 0, policy_version 590 (0.0013) [2024-08-04 13:13:22,332][00695] Fps is (10 sec: 19660.8, 60 sec: 19592.5, 300 sec: 18810.1). Total num frames: 2445312. Throughput: 0: 4881.8. Samples: 606020. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2024-08-04 13:13:22,334][00695] Avg episode reward: [(0, '6.969')] [2024-08-04 13:13:22,801][01565] Updated weights for policy 0, policy_version 600 (0.0013) [2024-08-04 13:13:24,838][01565] Updated weights for policy 0, policy_version 610 (0.0013) [2024-08-04 13:13:26,979][01565] Updated weights for policy 0, policy_version 620 (0.0013) [2024-08-04 13:13:27,332][00695] Fps is (10 sec: 19660.9, 60 sec: 19524.3, 300 sec: 18841.6). Total num frames: 2543616. Throughput: 0: 4889.3. Samples: 635622. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2024-08-04 13:13:27,334][00695] Avg episode reward: [(0, '7.280')] [2024-08-04 13:13:29,124][01565] Updated weights for policy 0, policy_version 630 (0.0013) [2024-08-04 13:13:31,180][01565] Updated weights for policy 0, policy_version 640 (0.0012) [2024-08-04 13:13:32,332][00695] Fps is (10 sec: 19660.8, 60 sec: 19524.3, 300 sec: 18870.9). Total num frames: 2641920. Throughput: 0: 4891.2. Samples: 650042. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2024-08-04 13:13:32,334][00695] Avg episode reward: [(0, '7.480')] [2024-08-04 13:13:33,233][01565] Updated weights for policy 0, policy_version 650 (0.0012) [2024-08-04 13:13:35,299][01565] Updated weights for policy 0, policy_version 660 (0.0013) [2024-08-04 13:13:37,332][00695] Fps is (10 sec: 19660.8, 60 sec: 19592.5, 300 sec: 18898.1). Total num frames: 2740224. Throughput: 0: 4907.4. Samples: 679828. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2024-08-04 13:13:37,334][00695] Avg episode reward: [(0, '8.700')] [2024-08-04 13:13:37,336][01552] Saving new best policy, reward=8.700! [2024-08-04 13:13:37,450][01565] Updated weights for policy 0, policy_version 670 (0.0012) [2024-08-04 13:13:39,478][01565] Updated weights for policy 0, policy_version 680 (0.0013) [2024-08-04 13:13:41,608][01565] Updated weights for policy 0, policy_version 690 (0.0013) [2024-08-04 13:13:42,332][00695] Fps is (10 sec: 19660.5, 60 sec: 19592.5, 300 sec: 18923.5). Total num frames: 2838528. Throughput: 0: 4900.5. Samples: 709018. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2024-08-04 13:13:42,335][00695] Avg episode reward: [(0, '9.553')] [2024-08-04 13:13:42,343][01552] Saving new best policy, reward=9.553! [2024-08-04 13:13:43,722][01565] Updated weights for policy 0, policy_version 700 (0.0013) [2024-08-04 13:13:45,770][01565] Updated weights for policy 0, policy_version 710 (0.0013) [2024-08-04 13:13:47,332][00695] Fps is (10 sec: 19661.0, 60 sec: 19660.8, 300 sec: 18947.3). Total num frames: 2936832. Throughput: 0: 4912.5. Samples: 723736. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2024-08-04 13:13:47,335][00695] Avg episode reward: [(0, '10.370')] [2024-08-04 13:13:47,337][01552] Saving new best policy, reward=10.370! [2024-08-04 13:13:47,845][01565] Updated weights for policy 0, policy_version 720 (0.0013) [2024-08-04 13:13:49,891][01565] Updated weights for policy 0, policy_version 730 (0.0013) [2024-08-04 13:13:51,936][01565] Updated weights for policy 0, policy_version 740 (0.0012) [2024-08-04 13:13:52,332][00695] Fps is (10 sec: 19661.1, 60 sec: 19592.5, 300 sec: 18969.6). Total num frames: 3035136. Throughput: 0: 4919.1. Samples: 753618. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) [2024-08-04 13:13:52,334][00695] Avg episode reward: [(0, '12.279')] [2024-08-04 13:13:52,347][01552] Saving new best policy, reward=12.279! [2024-08-04 13:13:54,032][01565] Updated weights for policy 0, policy_version 750 (0.0013) [2024-08-04 13:13:56,157][01565] Updated weights for policy 0, policy_version 760 (0.0012) [2024-08-04 13:13:57,332][00695] Fps is (10 sec: 19660.6, 60 sec: 19592.5, 300 sec: 18990.5). Total num frames: 3133440. Throughput: 0: 4909.2. Samples: 782954. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2024-08-04 13:13:57,335][00695] Avg episode reward: [(0, '11.959')] [2024-08-04 13:13:58,241][01565] Updated weights for policy 0, policy_version 770 (0.0013) [2024-08-04 13:14:00,290][01565] Updated weights for policy 0, policy_version 780 (0.0012) [2024-08-04 13:14:02,322][01565] Updated weights for policy 0, policy_version 790 (0.0013) [2024-08-04 13:14:02,332][00695] Fps is (10 sec: 20070.4, 60 sec: 19729.1, 300 sec: 19034.4). Total num frames: 3235840. Throughput: 0: 4921.9. Samples: 797878. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2024-08-04 13:14:02,334][00695] Avg episode reward: [(0, '13.126')] [2024-08-04 13:14:02,343][01552] Saving new best policy, reward=13.126! [2024-08-04 13:14:04,399][01565] Updated weights for policy 0, policy_version 800 (0.0013) [2024-08-04 13:14:06,469][01565] Updated weights for policy 0, policy_version 810 (0.0012) [2024-08-04 13:14:07,332][00695] Fps is (10 sec: 20070.4, 60 sec: 19729.1, 300 sec: 19052.3). Total num frames: 3334144. Throughput: 0: 4926.5. Samples: 827712. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2024-08-04 13:14:07,334][00695] Avg episode reward: [(0, '14.790')] [2024-08-04 13:14:07,337][01552] Saving new best policy, reward=14.790! [2024-08-04 13:14:08,562][01565] Updated weights for policy 0, policy_version 820 (0.0013) [2024-08-04 13:14:10,717][01565] Updated weights for policy 0, policy_version 830 (0.0013) [2024-08-04 13:14:12,332][00695] Fps is (10 sec: 19251.3, 60 sec: 19660.8, 300 sec: 19046.4). Total num frames: 3428352. Throughput: 0: 4916.9. Samples: 856884. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) [2024-08-04 13:14:12,334][00695] Avg episode reward: [(0, '16.816')] [2024-08-04 13:14:12,372][01552] Saving new best policy, reward=16.816! [2024-08-04 13:14:12,780][01565] Updated weights for policy 0, policy_version 840 (0.0013) [2024-08-04 13:14:14,802][01565] Updated weights for policy 0, policy_version 850 (0.0013) [2024-08-04 13:14:16,851][01565] Updated weights for policy 0, policy_version 860 (0.0013) [2024-08-04 13:14:17,332][00695] Fps is (10 sec: 19660.8, 60 sec: 19729.1, 300 sec: 19085.2). Total num frames: 3530752. Throughput: 0: 4931.7. Samples: 871968. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2024-08-04 13:14:17,335][00695] Avg episode reward: [(0, '17.412')] [2024-08-04 13:14:17,337][01552] Saving new best policy, reward=17.412! [2024-08-04 13:14:18,937][01565] Updated weights for policy 0, policy_version 870 (0.0013) [2024-08-04 13:14:21,051][01565] Updated weights for policy 0, policy_version 880 (0.0013) [2024-08-04 13:14:22,332][00695] Fps is (10 sec: 20070.8, 60 sec: 19729.1, 300 sec: 19100.3). Total num frames: 3629056. Throughput: 0: 4925.2. Samples: 901462. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) [2024-08-04 13:14:22,334][00695] Avg episode reward: [(0, '17.746')] [2024-08-04 13:14:22,343][01552] Saving new best policy, reward=17.746! [2024-08-04 13:14:23,184][01565] Updated weights for policy 0, policy_version 890 (0.0013) [2024-08-04 13:14:25,307][01565] Updated weights for policy 0, policy_version 900 (0.0013) [2024-08-04 13:14:27,332][00695] Fps is (10 sec: 19251.4, 60 sec: 19660.9, 300 sec: 19093.7). Total num frames: 3723264. Throughput: 0: 4923.4. Samples: 930568. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) [2024-08-04 13:14:27,334][00695] Avg episode reward: [(0, '17.407')] [2024-08-04 13:14:27,360][01565] Updated weights for policy 0, policy_version 910 (0.0013) [2024-08-04 13:14:29,389][01565] Updated weights for policy 0, policy_version 920 (0.0013) [2024-08-04 13:14:31,427][01565] Updated weights for policy 0, policy_version 930 (0.0013) [2024-08-04 13:14:32,332][00695] Fps is (10 sec: 19660.5, 60 sec: 19729.1, 300 sec: 19128.3). Total num frames: 3825664. Throughput: 0: 4934.0. Samples: 945768. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) [2024-08-04 13:14:32,334][00695] Avg episode reward: [(0, '17.492')] [2024-08-04 13:14:33,474][01565] Updated weights for policy 0, policy_version 940 (0.0013) [2024-08-04 13:14:35,537][01565] Updated weights for policy 0, policy_version 950 (0.0012) [2024-08-04 13:14:37,332][00695] Fps is (10 sec: 20069.6, 60 sec: 19729.0, 300 sec: 19141.3). Total num frames: 3923968. Throughput: 0: 4933.8. Samples: 975640. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2024-08-04 13:14:37,334][00695] Avg episode reward: [(0, '18.898')] [2024-08-04 13:14:37,337][01552] Saving new best policy, reward=18.898! [2024-08-04 13:14:37,659][01565] Updated weights for policy 0, policy_version 960 (0.0013) [2024-08-04 13:14:39,749][01565] Updated weights for policy 0, policy_version 970 (0.0013) [2024-08-04 13:14:41,351][01552] Stopping Batcher_0... [2024-08-04 13:14:41,352][01552] Loop batcher_evt_loop terminating... [2024-08-04 13:14:41,352][01552] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... [2024-08-04 13:14:41,351][00695] Component Batcher_0 stopped! [2024-08-04 13:14:41,368][01567] Stopping RolloutWorker_w1... [2024-08-04 13:14:41,368][01565] Weights refcount: 2 0 [2024-08-04 13:14:41,368][01567] Loop rollout_proc1_evt_loop terminating... [2024-08-04 13:14:41,369][01584] Stopping RolloutWorker_w7... [2024-08-04 13:14:41,369][01584] Loop rollout_proc7_evt_loop terminating... [2024-08-04 13:14:41,370][01570] Stopping RolloutWorker_w4... [2024-08-04 13:14:41,370][01565] Stopping InferenceWorker_p0-w0... [2024-08-04 13:14:41,370][01571] Stopping RolloutWorker_w5... [2024-08-04 13:14:41,370][01566] Stopping RolloutWorker_w0... [2024-08-04 13:14:41,370][01570] Loop rollout_proc4_evt_loop terminating... [2024-08-04 13:14:41,370][01565] Loop inference_proc0-0_evt_loop terminating... [2024-08-04 13:14:41,371][01571] Loop rollout_proc5_evt_loop terminating... [2024-08-04 13:14:41,371][01566] Loop rollout_proc0_evt_loop terminating... [2024-08-04 13:14:41,371][01569] Stopping RolloutWorker_w3... [2024-08-04 13:14:41,371][01568] Stopping RolloutWorker_w2... [2024-08-04 13:14:41,368][00695] Component RolloutWorker_w1 stopped! [2024-08-04 13:14:41,372][01569] Loop rollout_proc3_evt_loop terminating... [2024-08-04 13:14:41,372][01568] Loop rollout_proc2_evt_loop terminating... [2024-08-04 13:14:41,372][01572] Stopping RolloutWorker_w6... [2024-08-04 13:14:41,373][01572] Loop rollout_proc6_evt_loop terminating... [2024-08-04 13:14:41,372][00695] Component RolloutWorker_w7 stopped! [2024-08-04 13:14:41,374][00695] Component RolloutWorker_w4 stopped! [2024-08-04 13:14:41,377][00695] Component InferenceWorker_p0-w0 stopped! [2024-08-04 13:14:41,379][00695] Component RolloutWorker_w5 stopped! [2024-08-04 13:14:41,381][00695] Component RolloutWorker_w0 stopped! [2024-08-04 13:14:41,384][00695] Component RolloutWorker_w2 stopped! [2024-08-04 13:14:41,386][00695] Component RolloutWorker_w3 stopped! [2024-08-04 13:14:41,390][00695] Component RolloutWorker_w6 stopped! [2024-08-04 13:14:41,432][01552] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... [2024-08-04 13:14:41,571][01552] Stopping LearnerWorker_p0... [2024-08-04 13:14:41,572][01552] Loop learner_proc0_evt_loop terminating... [2024-08-04 13:14:41,571][00695] Component LearnerWorker_p0 stopped! [2024-08-04 13:14:41,573][00695] Waiting for process learner_proc0 to stop... [2024-08-04 13:14:42,318][00695] Waiting for process inference_proc0-0 to join... [2024-08-04 13:14:42,321][00695] Waiting for process rollout_proc0 to join... [2024-08-04 13:14:42,323][00695] Waiting for process rollout_proc1 to join... [2024-08-04 13:14:42,325][00695] Waiting for process rollout_proc2 to join... [2024-08-04 13:14:42,327][00695] Waiting for process rollout_proc3 to join... [2024-08-04 13:14:42,329][00695] Waiting for process rollout_proc4 to join... [2024-08-04 13:14:42,331][00695] Waiting for process rollout_proc5 to join... [2024-08-04 13:14:42,333][00695] Waiting for process rollout_proc6 to join... [2024-08-04 13:14:42,335][00695] Waiting for process rollout_proc7 to join... [2024-08-04 13:14:42,337][00695] Batcher 0 profile tree view: batching: 11.2535, releasing_batches: 0.0233 [2024-08-04 13:14:42,338][00695] InferenceWorker_p0-w0 profile tree view: wait_policy: 0.0001 wait_policy_total: 3.8458 update_model: 3.4116 weight_update: 0.0013 one_step: 0.0025 handle_policy_step: 190.4432 deserialize: 8.1147, stack: 1.2636, obs_to_device_normalize: 44.4669, forward: 93.7349, send_messages: 13.6313 prepare_outputs: 20.8268 to_cpu: 12.6829 [2024-08-04 13:14:42,339][00695] Learner 0 profile tree view: misc: 0.0045, prepare_batch: 10.1533 train: 23.3225 epoch_init: 0.0053, minibatch_init: 0.0062, losses_postprocess: 0.2948, kl_divergence: 0.4003, after_optimizer: 5.5042 calculate_losses: 9.6017 losses_init: 0.0033, forward_head: 0.6912, bptt_initial: 5.9082, tail: 0.5724, advantages_returns: 0.1480, losses: 1.0713 bptt: 1.0338 bptt_forward_core: 0.9800 update: 7.1648 clip: 0.7562 [2024-08-04 13:14:42,343][00695] RolloutWorker_w0 profile tree view: wait_for_trajectories: 0.1445, enqueue_policy_requests: 7.5329, env_step: 125.5377, overhead: 6.0592, complete_rollouts: 0.2284 save_policy_outputs: 8.7593 split_output_tensors: 3.4895 [2024-08-04 13:14:42,344][00695] RolloutWorker_w7 profile tree view: wait_for_trajectories: 0.1478, enqueue_policy_requests: 7.5370, env_step: 125.2499, overhead: 6.2157, complete_rollouts: 0.2298 save_policy_outputs: 8.7719 split_output_tensors: 3.5418 [2024-08-04 13:14:42,346][00695] Loop Runner_EvtLoop terminating... [2024-08-04 13:14:42,347][00695] Runner profile tree view: main_loop: 215.9152 [2024-08-04 13:14:42,349][00695] Collected {0: 4005888}, FPS: 18553.1 [2024-08-04 13:15:09,203][00695] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json [2024-08-04 13:15:09,204][00695] Overriding arg 'num_workers' with value 1 passed from command line [2024-08-04 13:15:09,206][00695] Adding new argument 'no_render'=True that is not in the saved config file! [2024-08-04 13:15:09,208][00695] Adding new argument 'save_video'=True that is not in the saved config file! [2024-08-04 13:15:09,208][00695] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! [2024-08-04 13:15:09,211][00695] Adding new argument 'video_name'=None that is not in the saved config file! [2024-08-04 13:15:09,212][00695] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file! [2024-08-04 13:15:09,213][00695] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! [2024-08-04 13:15:09,214][00695] Adding new argument 'push_to_hub'=False that is not in the saved config file! [2024-08-04 13:15:09,215][00695] Adding new argument 'hf_repository'=None that is not in the saved config file! [2024-08-04 13:15:09,217][00695] Adding new argument 'policy_index'=0 that is not in the saved config file! [2024-08-04 13:15:09,217][00695] Adding new argument 'eval_deterministic'=False that is not in the saved config file! [2024-08-04 13:15:09,219][00695] Adding new argument 'train_script'=None that is not in the saved config file! [2024-08-04 13:15:09,220][00695] Adding new argument 'enjoy_script'=None that is not in the saved config file! [2024-08-04 13:15:09,222][00695] Using frameskip 1 and render_action_repeat=4 for evaluation [2024-08-04 13:15:09,250][00695] Doom resolution: 160x120, resize resolution: (128, 72) [2024-08-04 13:15:09,253][00695] RunningMeanStd input shape: (3, 72, 128) [2024-08-04 13:15:09,255][00695] RunningMeanStd input shape: (1,) [2024-08-04 13:15:09,271][00695] ConvEncoder: input_channels=3 [2024-08-04 13:15:09,384][00695] Conv encoder output size: 512 [2024-08-04 13:15:09,385][00695] Policy head output size: 512 [2024-08-04 13:15:09,532][00695] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... [2024-08-04 13:15:10,343][00695] Num frames 100... [2024-08-04 13:15:10,464][00695] Num frames 200... [2024-08-04 13:15:10,581][00695] Num frames 300... [2024-08-04 13:15:10,702][00695] Num frames 400... [2024-08-04 13:15:10,818][00695] Num frames 500... [2024-08-04 13:15:10,936][00695] Num frames 600... [2024-08-04 13:15:11,054][00695] Num frames 700... [2024-08-04 13:15:11,114][00695] Avg episode rewards: #0: 11.040, true rewards: #0: 7.040 [2024-08-04 13:15:11,115][00695] Avg episode reward: 11.040, avg true_objective: 7.040 [2024-08-04 13:15:11,229][00695] Num frames 800... [2024-08-04 13:15:11,349][00695] Num frames 900... [2024-08-04 13:15:11,468][00695] Num frames 1000... [2024-08-04 13:15:11,586][00695] Num frames 1100... [2024-08-04 13:15:11,706][00695] Num frames 1200... [2024-08-04 13:15:11,827][00695] Num frames 1300... [2024-08-04 13:15:11,949][00695] Num frames 1400... [2024-08-04 13:15:12,081][00695] Num frames 1500... [2024-08-04 13:15:12,201][00695] Num frames 1600... [2024-08-04 13:15:12,253][00695] Avg episode rewards: #0: 14.000, true rewards: #0: 8.000 [2024-08-04 13:15:12,255][00695] Avg episode reward: 14.000, avg true_objective: 8.000 [2024-08-04 13:15:12,373][00695] Num frames 1700... [2024-08-04 13:15:12,490][00695] Num frames 1800... [2024-08-04 13:15:12,608][00695] Num frames 1900... [2024-08-04 13:15:12,731][00695] Num frames 2000... [2024-08-04 13:15:12,858][00695] Num frames 2100... [2024-08-04 13:15:12,985][00695] Num frames 2200... [2024-08-04 13:15:13,114][00695] Num frames 2300... [2024-08-04 13:15:13,233][00695] Num frames 2400... [2024-08-04 13:15:13,351][00695] Num frames 2500... [2024-08-04 13:15:13,437][00695] Avg episode rewards: #0: 14.760, true rewards: #0: 8.427 [2024-08-04 13:15:13,439][00695] Avg episode reward: 14.760, avg true_objective: 8.427 [2024-08-04 13:15:13,524][00695] Num frames 2600... [2024-08-04 13:15:13,643][00695] Num frames 2700... [2024-08-04 13:15:13,760][00695] Num frames 2800... [2024-08-04 13:15:13,878][00695] Num frames 2900... [2024-08-04 13:15:13,997][00695] Num frames 3000... [2024-08-04 13:15:14,116][00695] Num frames 3100... [2024-08-04 13:15:14,236][00695] Num frames 3200... [2024-08-04 13:15:14,354][00695] Num frames 3300... [2024-08-04 13:15:14,485][00695] Avg episode rewards: #0: 15.408, true rewards: #0: 8.407 [2024-08-04 13:15:14,486][00695] Avg episode reward: 15.408, avg true_objective: 8.407 [2024-08-04 13:15:14,531][00695] Num frames 3400... [2024-08-04 13:15:14,649][00695] Num frames 3500... [2024-08-04 13:15:14,768][00695] Num frames 3600... [2024-08-04 13:15:14,884][00695] Num frames 3700... [2024-08-04 13:15:15,002][00695] Num frames 3800... [2024-08-04 13:15:15,123][00695] Num frames 3900... [2024-08-04 13:15:15,240][00695] Num frames 4000... [2024-08-04 13:15:15,360][00695] Num frames 4100... [2024-08-04 13:15:15,479][00695] Num frames 4200... [2024-08-04 13:15:15,596][00695] Num frames 4300... [2024-08-04 13:15:15,717][00695] Num frames 4400... [2024-08-04 13:15:15,887][00695] Avg episode rewards: #0: 17.592, true rewards: #0: 8.992 [2024-08-04 13:15:15,888][00695] Avg episode reward: 17.592, avg true_objective: 8.992 [2024-08-04 13:15:15,895][00695] Num frames 4500... [2024-08-04 13:15:16,014][00695] Num frames 4600... [2024-08-04 13:15:16,131][00695] Num frames 4700... [2024-08-04 13:15:16,246][00695] Num frames 4800... [2024-08-04 13:15:16,363][00695] Num frames 4900... [2024-08-04 13:15:16,479][00695] Num frames 5000... [2024-08-04 13:15:16,597][00695] Num frames 5100... [2024-08-04 13:15:16,714][00695] Num frames 5200... [2024-08-04 13:15:16,841][00695] Avg episode rewards: #0: 16.773, true rewards: #0: 8.773 [2024-08-04 13:15:16,842][00695] Avg episode reward: 16.773, avg true_objective: 8.773 [2024-08-04 13:15:16,887][00695] Num frames 5300... [2024-08-04 13:15:17,010][00695] Num frames 5400... [2024-08-04 13:15:17,126][00695] Num frames 5500... [2024-08-04 13:15:17,246][00695] Num frames 5600... [2024-08-04 13:15:17,362][00695] Num frames 5700... [2024-08-04 13:15:17,479][00695] Num frames 5800... [2024-08-04 13:15:17,598][00695] Num frames 5900... [2024-08-04 13:15:17,715][00695] Num frames 6000... [2024-08-04 13:15:17,832][00695] Num frames 6100... [2024-08-04 13:15:17,953][00695] Num frames 6200... [2024-08-04 13:15:18,071][00695] Num frames 6300... [2024-08-04 13:15:18,190][00695] Num frames 6400... [2024-08-04 13:15:18,310][00695] Num frames 6500... [2024-08-04 13:15:18,453][00695] Avg episode rewards: #0: 18.966, true rewards: #0: 9.394 [2024-08-04 13:15:18,455][00695] Avg episode reward: 18.966, avg true_objective: 9.394 [2024-08-04 13:15:18,485][00695] Num frames 6600... [2024-08-04 13:15:18,600][00695] Num frames 6700... [2024-08-04 13:15:18,720][00695] Num frames 6800... [2024-08-04 13:15:18,837][00695] Num frames 6900... [2024-08-04 13:15:18,951][00695] Num frames 7000... [2024-08-04 13:15:19,066][00695] Num frames 7100... [2024-08-04 13:15:19,157][00695] Avg episode rewards: #0: 17.788, true rewards: #0: 8.912 [2024-08-04 13:15:19,159][00695] Avg episode reward: 17.788, avg true_objective: 8.912 [2024-08-04 13:15:19,241][00695] Num frames 7200... [2024-08-04 13:15:19,359][00695] Num frames 7300... [2024-08-04 13:15:19,476][00695] Num frames 7400... [2024-08-04 13:15:19,592][00695] Num frames 7500... [2024-08-04 13:15:19,707][00695] Num frames 7600... [2024-08-04 13:15:19,826][00695] Num frames 7700... [2024-08-04 13:15:19,946][00695] Num frames 7800... [2024-08-04 13:15:20,063][00695] Num frames 7900... [2024-08-04 13:15:20,182][00695] Num frames 8000... [2024-08-04 13:15:20,299][00695] Num frames 8100... [2024-08-04 13:15:20,416][00695] Num frames 8200... [2024-08-04 13:15:20,567][00695] Avg episode rewards: #0: 18.425, true rewards: #0: 9.202 [2024-08-04 13:15:20,569][00695] Avg episode reward: 18.425, avg true_objective: 9.202 [2024-08-04 13:15:20,590][00695] Num frames 8300... [2024-08-04 13:15:20,707][00695] Num frames 8400... [2024-08-04 13:15:20,920][00695] Num frames 8500... [2024-08-04 13:15:21,036][00695] Num frames 8600... [2024-08-04 13:15:21,153][00695] Num frames 8700... [2024-08-04 13:15:21,271][00695] Num frames 8800... [2024-08-04 13:15:21,390][00695] Num frames 8900... [2024-08-04 13:15:21,509][00695] Num frames 9000... [2024-08-04 13:15:21,628][00695] Num frames 9100... [2024-08-04 13:15:21,748][00695] Num frames 9200... [2024-08-04 13:15:21,865][00695] Num frames 9300... [2024-08-04 13:15:22,009][00695] Avg episode rewards: #0: 18.775, true rewards: #0: 9.375 [2024-08-04 13:15:22,010][00695] Avg episode reward: 18.775, avg true_objective: 9.375 [2024-08-04 13:15:44,218][00695] Replay video saved to /content/train_dir/default_experiment/replay.mp4! [2024-08-04 13:19:20,884][00695] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json [2024-08-04 13:19:20,885][00695] Overriding arg 'num_workers' with value 1 passed from command line [2024-08-04 13:19:20,886][00695] Adding new argument 'no_render'=True that is not in the saved config file! [2024-08-04 13:19:20,888][00695] Adding new argument 'save_video'=True that is not in the saved config file! [2024-08-04 13:19:20,889][00695] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! [2024-08-04 13:19:20,890][00695] Adding new argument 'video_name'=None that is not in the saved config file! [2024-08-04 13:19:20,892][00695] Adding new argument 'max_num_frames'=100000 that is not in the saved config file! [2024-08-04 13:19:20,893][00695] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! [2024-08-04 13:19:20,894][00695] Adding new argument 'push_to_hub'=True that is not in the saved config file! [2024-08-04 13:19:20,895][00695] Adding new argument 'hf_repository'='dogukankartal/SampleFactory_ViZDoom' that is not in the saved config file! [2024-08-04 13:19:20,896][00695] Adding new argument 'policy_index'=0 that is not in the saved config file! [2024-08-04 13:19:20,897][00695] Adding new argument 'eval_deterministic'=False that is not in the saved config file! [2024-08-04 13:19:20,898][00695] Adding new argument 'train_script'=None that is not in the saved config file! [2024-08-04 13:19:20,901][00695] Adding new argument 'enjoy_script'=None that is not in the saved config file! [2024-08-04 13:19:20,902][00695] Using frameskip 1 and render_action_repeat=4 for evaluation [2024-08-04 13:19:20,924][00695] RunningMeanStd input shape: (3, 72, 128) [2024-08-04 13:19:20,926][00695] RunningMeanStd input shape: (1,) [2024-08-04 13:19:20,937][00695] ConvEncoder: input_channels=3 [2024-08-04 13:19:20,976][00695] Conv encoder output size: 512 [2024-08-04 13:19:20,977][00695] Policy head output size: 512 [2024-08-04 13:19:20,998][00695] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... [2024-08-04 13:19:21,418][00695] Num frames 100... [2024-08-04 13:19:21,534][00695] Num frames 200... [2024-08-04 13:19:21,653][00695] Num frames 300... [2024-08-04 13:19:21,769][00695] Num frames 400... [2024-08-04 13:19:21,879][00695] Avg episode rewards: #0: 7.480, true rewards: #0: 4.480 [2024-08-04 13:19:21,880][00695] Avg episode reward: 7.480, avg true_objective: 4.480 [2024-08-04 13:19:21,943][00695] Num frames 500... [2024-08-04 13:19:22,059][00695] Num frames 600... [2024-08-04 13:19:22,175][00695] Num frames 700... [2024-08-04 13:19:22,292][00695] Num frames 800... [2024-08-04 13:19:22,410][00695] Num frames 900... [2024-08-04 13:19:22,528][00695] Num frames 1000... [2024-08-04 13:19:22,645][00695] Num frames 1100... [2024-08-04 13:19:22,763][00695] Num frames 1200... [2024-08-04 13:19:22,884][00695] Num frames 1300... [2024-08-04 13:19:23,004][00695] Num frames 1400... [2024-08-04 13:19:23,125][00695] Num frames 1500... [2024-08-04 13:19:23,242][00695] Num frames 1600... [2024-08-04 13:19:23,361][00695] Num frames 1700... [2024-08-04 13:19:23,478][00695] Num frames 1800... [2024-08-04 13:19:23,598][00695] Num frames 1900... [2024-08-04 13:19:23,717][00695] Num frames 2000... [2024-08-04 13:19:23,838][00695] Num frames 2100... [2024-08-04 13:19:23,959][00695] Num frames 2200... [2024-08-04 13:19:24,085][00695] Num frames 2300... [2024-08-04 13:19:24,213][00695] Num frames 2400... [2024-08-04 13:19:24,342][00695] Num frames 2500... [2024-08-04 13:19:24,457][00695] Avg episode rewards: #0: 29.240, true rewards: #0: 12.740 [2024-08-04 13:19:24,458][00695] Avg episode reward: 29.240, avg true_objective: 12.740 [2024-08-04 13:19:24,525][00695] Num frames 2600... [2024-08-04 13:19:24,652][00695] Num frames 2700... [2024-08-04 13:19:24,778][00695] Num frames 2800... [2024-08-04 13:19:24,902][00695] Num frames 2900... [2024-08-04 13:19:25,028][00695] Num frames 3000... [2024-08-04 13:19:25,153][00695] Num frames 3100... [2024-08-04 13:19:25,281][00695] Num frames 3200... [2024-08-04 13:19:25,401][00695] Avg episode rewards: #0: 24.506, true rewards: #0: 10.840 [2024-08-04 13:19:25,402][00695] Avg episode reward: 24.506, avg true_objective: 10.840 [2024-08-04 13:19:25,465][00695] Num frames 3300... [2024-08-04 13:19:25,590][00695] Num frames 3400... [2024-08-04 13:19:25,717][00695] Num frames 3500... [2024-08-04 13:19:25,840][00695] Num frames 3600... [2024-08-04 13:19:25,957][00695] Num frames 3700... [2024-08-04 13:19:26,085][00695] Num frames 3800... [2024-08-04 13:19:26,211][00695] Num frames 3900... [2024-08-04 13:19:26,337][00695] Num frames 4000... [2024-08-04 13:19:26,462][00695] Num frames 4100... [2024-08-04 13:19:26,673][00695] Num frames 4200... [2024-08-04 13:19:26,799][00695] Num frames 4300... [2024-08-04 13:19:26,864][00695] Avg episode rewards: #0: 25.270, true rewards: #0: 10.770 [2024-08-04 13:19:26,866][00695] Avg episode reward: 25.270, avg true_objective: 10.770 [2024-08-04 13:19:26,979][00695] Num frames 4400... [2024-08-04 13:19:27,106][00695] Num frames 4500... [2024-08-04 13:19:27,232][00695] Num frames 4600... [2024-08-04 13:19:27,356][00695] Num frames 4700... [2024-08-04 13:19:27,481][00695] Num frames 4800... [2024-08-04 13:19:27,608][00695] Num frames 4900... [2024-08-04 13:19:27,735][00695] Num frames 5000... [2024-08-04 13:19:27,860][00695] Num frames 5100... [2024-08-04 13:19:27,985][00695] Num frames 5200... [2024-08-04 13:19:28,113][00695] Num frames 5300... [2024-08-04 13:19:28,238][00695] Num frames 5400... [2024-08-04 13:19:28,363][00695] Num frames 5500... [2024-08-04 13:19:28,485][00695] Num frames 5600... [2024-08-04 13:19:28,609][00695] Num frames 5700... [2024-08-04 13:19:28,734][00695] Num frames 5800... [2024-08-04 13:19:28,857][00695] Num frames 5900... [2024-08-04 13:19:28,997][00695] Avg episode rewards: #0: 28.144, true rewards: #0: 11.944 [2024-08-04 13:19:28,998][00695] Avg episode reward: 28.144, avg true_objective: 11.944 [2024-08-04 13:19:29,033][00695] Num frames 6000... [2024-08-04 13:19:29,150][00695] Num frames 6100... [2024-08-04 13:19:29,271][00695] Num frames 6200... [2024-08-04 13:19:29,389][00695] Num frames 6300... [2024-08-04 13:19:29,505][00695] Num frames 6400... [2024-08-04 13:19:29,584][00695] Avg episode rewards: #0: 24.700, true rewards: #0: 10.700 [2024-08-04 13:19:29,585][00695] Avg episode reward: 24.700, avg true_objective: 10.700 [2024-08-04 13:19:29,676][00695] Num frames 6500... [2024-08-04 13:19:29,794][00695] Num frames 6600... [2024-08-04 13:19:29,910][00695] Num frames 6700... [2024-08-04 13:19:30,029][00695] Num frames 6800... [2024-08-04 13:19:30,154][00695] Num frames 6900... [2024-08-04 13:19:30,281][00695] Num frames 7000... [2024-08-04 13:19:30,406][00695] Num frames 7100... [2024-08-04 13:19:30,530][00695] Num frames 7200... [2024-08-04 13:19:30,653][00695] Num frames 7300... [2024-08-04 13:19:30,759][00695] Avg episode rewards: #0: 24.058, true rewards: #0: 10.487 [2024-08-04 13:19:30,761][00695] Avg episode reward: 24.058, avg true_objective: 10.487 [2024-08-04 13:19:30,834][00695] Num frames 7400... [2024-08-04 13:19:30,957][00695] Num frames 7500... [2024-08-04 13:19:31,079][00695] Num frames 7600... [2024-08-04 13:19:31,197][00695] Num frames 7700... [2024-08-04 13:19:31,317][00695] Num frames 7800... [2024-08-04 13:19:31,441][00695] Num frames 7900... [2024-08-04 13:19:31,565][00695] Num frames 8000... [2024-08-04 13:19:31,692][00695] Num frames 8100... [2024-08-04 13:19:31,839][00695] Avg episode rewards: #0: 23.466, true rewards: #0: 10.216 [2024-08-04 13:19:31,841][00695] Avg episode reward: 23.466, avg true_objective: 10.216 [2024-08-04 13:19:31,875][00695] Num frames 8200... [2024-08-04 13:19:32,000][00695] Num frames 8300... [2024-08-04 13:19:32,125][00695] Num frames 8400... [2024-08-04 13:19:32,251][00695] Num frames 8500... [2024-08-04 13:19:32,375][00695] Num frames 8600... [2024-08-04 13:19:32,501][00695] Num frames 8700... [2024-08-04 13:19:32,628][00695] Num frames 8800... [2024-08-04 13:19:32,753][00695] Num frames 8900... [2024-08-04 13:19:32,871][00695] Num frames 9000... [2024-08-04 13:19:32,989][00695] Num frames 9100... [2024-08-04 13:19:33,047][00695] Avg episode rewards: #0: 23.001, true rewards: #0: 10.112 [2024-08-04 13:19:33,048][00695] Avg episode reward: 23.001, avg true_objective: 10.112 [2024-08-04 13:19:33,163][00695] Num frames 9200... [2024-08-04 13:19:33,278][00695] Num frames 9300... [2024-08-04 13:19:33,393][00695] Num frames 9400... [2024-08-04 13:19:33,513][00695] Num frames 9500... [2024-08-04 13:19:33,628][00695] Num frames 9600... [2024-08-04 13:19:33,746][00695] Num frames 9700... [2024-08-04 13:19:33,863][00695] Num frames 9800... [2024-08-04 13:19:33,925][00695] Avg episode rewards: #0: 21.905, true rewards: #0: 9.805 [2024-08-04 13:19:33,927][00695] Avg episode reward: 21.905, avg true_objective: 9.805 [2024-08-04 13:19:56,989][00695] Replay video saved to /content/train_dir/default_experiment/replay.mp4! [2024-08-04 13:20:17,944][00695] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json [2024-08-04 13:20:17,945][00695] Overriding arg 'num_workers' with value 1 passed from command line [2024-08-04 13:20:17,946][00695] Adding new argument 'no_render'=True that is not in the saved config file! [2024-08-04 13:20:17,947][00695] Adding new argument 'save_video'=True that is not in the saved config file! [2024-08-04 13:20:17,949][00695] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! [2024-08-04 13:20:17,951][00695] Adding new argument 'video_name'=None that is not in the saved config file! [2024-08-04 13:20:17,952][00695] Adding new argument 'max_num_frames'=100000 that is not in the saved config file! [2024-08-04 13:20:17,953][00695] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! [2024-08-04 13:20:17,955][00695] Adding new argument 'push_to_hub'=True that is not in the saved config file! [2024-08-04 13:20:17,956][00695] Adding new argument 'hf_repository'='dogukankartal/SampleFactory_ViZDoom' that is not in the saved config file! [2024-08-04 13:20:17,957][00695] Adding new argument 'policy_index'=0 that is not in the saved config file! [2024-08-04 13:20:17,959][00695] Adding new argument 'eval_deterministic'=False that is not in the saved config file! [2024-08-04 13:20:17,960][00695] Adding new argument 'train_script'=None that is not in the saved config file! [2024-08-04 13:20:17,961][00695] Adding new argument 'enjoy_script'=None that is not in the saved config file! [2024-08-04 13:20:17,963][00695] Using frameskip 1 and render_action_repeat=4 for evaluation [2024-08-04 13:20:17,991][00695] RunningMeanStd input shape: (3, 72, 128) [2024-08-04 13:20:17,993][00695] RunningMeanStd input shape: (1,) [2024-08-04 13:20:18,005][00695] ConvEncoder: input_channels=3 [2024-08-04 13:20:18,042][00695] Conv encoder output size: 512 [2024-08-04 13:20:18,044][00695] Policy head output size: 512 [2024-08-04 13:20:18,063][00695] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... [2024-08-04 13:20:18,477][00695] Num frames 100... [2024-08-04 13:20:18,593][00695] Num frames 200... [2024-08-04 13:20:18,710][00695] Num frames 300... [2024-08-04 13:20:18,830][00695] Num frames 400... [2024-08-04 13:20:18,941][00695] Avg episode rewards: #0: 5.480, true rewards: #0: 4.480 [2024-08-04 13:20:18,943][00695] Avg episode reward: 5.480, avg true_objective: 4.480 [2024-08-04 13:20:19,011][00695] Num frames 500... [2024-08-04 13:20:19,131][00695] Num frames 600... [2024-08-04 13:20:19,246][00695] Num frames 700... [2024-08-04 13:20:19,363][00695] Num frames 800... [2024-08-04 13:20:19,483][00695] Num frames 900... [2024-08-04 13:20:19,598][00695] Num frames 1000... [2024-08-04 13:20:19,720][00695] Num frames 1100... [2024-08-04 13:20:19,839][00695] Num frames 1200... [2024-08-04 13:20:19,959][00695] Num frames 1300... [2024-08-04 13:20:20,087][00695] Num frames 1400... [2024-08-04 13:20:20,206][00695] Num frames 1500... [2024-08-04 13:20:20,327][00695] Num frames 1600... [2024-08-04 13:20:20,449][00695] Num frames 1700... [2024-08-04 13:20:20,572][00695] Num frames 1800... [2024-08-04 13:20:20,692][00695] Num frames 1900... [2024-08-04 13:20:20,816][00695] Num frames 2000... [2024-08-04 13:20:20,941][00695] Num frames 2100... [2024-08-04 13:20:21,087][00695] Avg episode rewards: #0: 25.380, true rewards: #0: 10.880 [2024-08-04 13:20:21,089][00695] Avg episode reward: 25.380, avg true_objective: 10.880 [2024-08-04 13:20:21,132][00695] Num frames 2200... [2024-08-04 13:20:21,255][00695] Num frames 2300... [2024-08-04 13:20:21,378][00695] Num frames 2400... [2024-08-04 13:20:21,498][00695] Num frames 2500... [2024-08-04 13:20:21,619][00695] Num frames 2600... [2024-08-04 13:20:21,740][00695] Num frames 2700... [2024-08-04 13:20:21,862][00695] Num frames 2800... [2024-08-04 13:20:21,936][00695] Avg episode rewards: #0: 20.717, true rewards: #0: 9.383 [2024-08-04 13:20:21,937][00695] Avg episode reward: 20.717, avg true_objective: 9.383 [2024-08-04 13:20:22,038][00695] Num frames 2900... [2024-08-04 13:20:22,159][00695] Num frames 3000... [2024-08-04 13:20:22,275][00695] Num frames 3100... [2024-08-04 13:20:22,396][00695] Num frames 3200... [2024-08-04 13:20:22,516][00695] Num frames 3300... [2024-08-04 13:20:22,635][00695] Num frames 3400... [2024-08-04 13:20:22,756][00695] Num frames 3500... [2024-08-04 13:20:22,876][00695] Num frames 3600... [2024-08-04 13:20:22,997][00695] Num frames 3700... [2024-08-04 13:20:23,120][00695] Num frames 3800... [2024-08-04 13:20:23,240][00695] Num frames 3900... [2024-08-04 13:20:23,375][00695] Avg episode rewards: #0: 22.417, true rewards: #0: 9.917 [2024-08-04 13:20:23,377][00695] Avg episode reward: 22.417, avg true_objective: 9.917 [2024-08-04 13:20:23,419][00695] Num frames 4000... [2024-08-04 13:20:23,540][00695] Num frames 4100... [2024-08-04 13:20:23,664][00695] Num frames 4200... [2024-08-04 13:20:23,783][00695] Num frames 4300... [2024-08-04 13:20:23,901][00695] Num frames 4400... [2024-08-04 13:20:23,975][00695] Avg episode rewards: #0: 19.030, true rewards: #0: 8.830 [2024-08-04 13:20:23,976][00695] Avg episode reward: 19.030, avg true_objective: 8.830 [2024-08-04 13:20:24,079][00695] Num frames 4500... [2024-08-04 13:20:24,198][00695] Num frames 4600... [2024-08-04 13:20:24,323][00695] Num frames 4700... [2024-08-04 13:20:24,450][00695] Num frames 4800... [2024-08-04 13:20:24,571][00695] Num frames 4900... [2024-08-04 13:20:24,693][00695] Num frames 5000... [2024-08-04 13:20:24,815][00695] Avg episode rewards: #0: 17.758, true rewards: #0: 8.425 [2024-08-04 13:20:24,816][00695] Avg episode reward: 17.758, avg true_objective: 8.425 [2024-08-04 13:20:24,870][00695] Num frames 5100... [2024-08-04 13:20:24,987][00695] Num frames 5200... [2024-08-04 13:20:25,109][00695] Num frames 5300... [2024-08-04 13:20:25,227][00695] Num frames 5400... [2024-08-04 13:20:25,343][00695] Num frames 5500... [2024-08-04 13:20:25,462][00695] Num frames 5600... [2024-08-04 13:20:25,577][00695] Num frames 5700... [2024-08-04 13:20:25,701][00695] Avg episode rewards: #0: 16.799, true rewards: #0: 8.227 [2024-08-04 13:20:25,703][00695] Avg episode reward: 16.799, avg true_objective: 8.227 [2024-08-04 13:20:25,751][00695] Num frames 5800... [2024-08-04 13:20:25,868][00695] Num frames 5900... [2024-08-04 13:20:25,990][00695] Num frames 6000... [2024-08-04 13:20:26,107][00695] Num frames 6100... [2024-08-04 13:20:26,224][00695] Num frames 6200... [2024-08-04 13:20:26,343][00695] Num frames 6300... [2024-08-04 13:20:26,460][00695] Num frames 6400... [2024-08-04 13:20:26,581][00695] Num frames 6500... [2024-08-04 13:20:26,700][00695] Num frames 6600... [2024-08-04 13:20:26,819][00695] Num frames 6700... [2024-08-04 13:20:26,938][00695] Num frames 6800... [2024-08-04 13:20:27,059][00695] Num frames 6900... [2024-08-04 13:20:27,207][00695] Avg episode rewards: #0: 18.225, true rewards: #0: 8.725 [2024-08-04 13:20:27,208][00695] Avg episode reward: 18.225, avg true_objective: 8.725 [2024-08-04 13:20:27,235][00695] Num frames 7000... [2024-08-04 13:20:27,351][00695] Num frames 7100... [2024-08-04 13:20:27,468][00695] Num frames 7200... [2024-08-04 13:20:27,588][00695] Num frames 7300... [2024-08-04 13:20:27,704][00695] Num frames 7400... [2024-08-04 13:20:27,822][00695] Num frames 7500... [2024-08-04 13:20:27,938][00695] Num frames 7600... [2024-08-04 13:20:28,150][00695] Num frames 7700... [2024-08-04 13:20:28,268][00695] Num frames 7800... [2024-08-04 13:20:28,387][00695] Num frames 7900... [2024-08-04 13:20:28,505][00695] Num frames 8000... [2024-08-04 13:20:28,622][00695] Num frames 8100... [2024-08-04 13:20:28,739][00695] Num frames 8200... [2024-08-04 13:20:28,858][00695] Num frames 8300... [2024-08-04 13:20:28,977][00695] Num frames 8400... [2024-08-04 13:20:29,095][00695] Avg episode rewards: #0: 19.836, true rewards: #0: 9.391 [2024-08-04 13:20:29,096][00695] Avg episode reward: 19.836, avg true_objective: 9.391 [2024-08-04 13:20:29,155][00695] Num frames 8500... [2024-08-04 13:20:29,274][00695] Num frames 8600... [2024-08-04 13:20:29,394][00695] Num frames 8700... [2024-08-04 13:20:29,515][00695] Num frames 8800... [2024-08-04 13:20:29,635][00695] Num frames 8900... [2024-08-04 13:20:29,757][00695] Num frames 9000... [2024-08-04 13:20:29,878][00695] Num frames 9100... [2024-08-04 13:20:29,997][00695] Num frames 9200... [2024-08-04 13:20:30,114][00695] Num frames 9300... [2024-08-04 13:20:30,233][00695] Num frames 9400... [2024-08-04 13:20:30,352][00695] Num frames 9500... [2024-08-04 13:20:30,472][00695] Num frames 9600... [2024-08-04 13:20:30,591][00695] Num frames 9700... [2024-08-04 13:20:30,713][00695] Num frames 9800... [2024-08-04 13:20:30,834][00695] Num frames 9900... [2024-08-04 13:20:30,969][00695] Avg episode rewards: #0: 21.365, true rewards: #0: 9.965 [2024-08-04 13:20:30,970][00695] Avg episode reward: 21.365, avg true_objective: 9.965 [2024-08-04 13:20:54,302][00695] Replay video saved to /content/train_dir/default_experiment/replay.mp4!