[2024-12-07 14:34:08,376][19013] Saving configuration to /home/rahatchd/code/deep-rl-hugging-face/unit8/train_dir/default_experiment/config.json... [2024-12-07 14:34:08,376][19013] Rollout worker 0 uses device cpu [2024-12-07 14:34:08,377][19013] Rollout worker 1 uses device cpu [2024-12-07 14:34:08,377][19013] Rollout worker 2 uses device cpu [2024-12-07 14:34:08,377][19013] Rollout worker 3 uses device cpu [2024-12-07 14:34:08,378][19013] Rollout worker 4 uses device cpu [2024-12-07 14:34:08,378][19013] Rollout worker 5 uses device cpu [2024-12-07 14:34:08,378][19013] Rollout worker 6 uses device cpu [2024-12-07 14:34:08,379][19013] Rollout worker 7 uses device cpu [2024-12-07 14:34:08,444][19013] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2024-12-07 14:34:08,444][19013] InferenceWorker_p0-w0: min num requests: 2 [2024-12-07 14:34:08,474][19013] Starting all processes... [2024-12-07 14:34:08,474][19013] Starting process learner_proc0 [2024-12-07 14:34:08,524][19013] Starting all processes... [2024-12-07 14:34:08,528][19013] Starting process inference_proc0-0 [2024-12-07 14:34:08,528][19013] Starting process rollout_proc0 [2024-12-07 14:34:08,528][19013] Starting process rollout_proc1 [2024-12-07 14:34:08,529][19013] Starting process rollout_proc2 [2024-12-07 14:34:08,529][19013] Starting process rollout_proc3 [2024-12-07 14:34:08,529][19013] Starting process rollout_proc4 [2024-12-07 14:34:08,530][19013] Starting process rollout_proc5 [2024-12-07 14:34:08,530][19013] Starting process rollout_proc6 [2024-12-07 14:34:08,530][19013] Starting process rollout_proc7 [2024-12-07 14:34:09,922][23822] Worker 3 uses CPU cores [9, 10, 11] [2024-12-07 14:34:09,956][23826] Worker 7 uses CPU cores [21, 22, 23] [2024-12-07 14:34:09,973][23806] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2024-12-07 14:34:09,973][23806] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0 [2024-12-07 14:34:09,975][23824] Worker 5 uses CPU cores [15, 16, 17] [2024-12-07 14:34:09,976][23827] Worker 6 uses CPU cores [18, 19, 20] [2024-12-07 14:34:09,988][23806] Num visible devices: 1 [2024-12-07 14:34:09,994][23825] Worker 2 uses CPU cores [6, 7, 8] [2024-12-07 14:34:09,996][23820] Worker 0 uses CPU cores [0, 1, 2] [2024-12-07 14:34:10,005][23806] Starting seed is not provided [2024-12-07 14:34:10,005][23806] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2024-12-07 14:34:10,006][23806] Initializing actor-critic model on device cuda:0 [2024-12-07 14:34:10,006][23806] RunningMeanStd input shape: (3, 72, 128) [2024-12-07 14:34:10,007][23806] RunningMeanStd input shape: (1,) [2024-12-07 14:34:10,015][23806] ConvEncoder: input_channels=3 [2024-12-07 14:34:10,022][23821] Worker 1 uses CPU cores [3, 4, 5] [2024-12-07 14:34:10,045][23823] Worker 4 uses CPU cores [12, 13, 14] [2024-12-07 14:34:10,090][23819] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2024-12-07 14:34:10,091][23819] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0 [2024-12-07 14:34:10,105][23819] Num visible devices: 1 [2024-12-07 14:34:10,112][23806] Conv encoder output size: 512 [2024-12-07 14:34:10,113][23806] Policy head output size: 512 [2024-12-07 14:34:10,131][23806] Created Actor Critic model with architecture: [2024-12-07 14:34:10,131][23806] 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) ) ) [2024-12-07 14:34:10,276][23806] Using optimizer [2024-12-07 14:34:11,044][23806] No checkpoints found [2024-12-07 14:34:11,044][23806] Did not load from checkpoint, starting from scratch! [2024-12-07 14:34:11,044][23806] Initialized policy 0 weights for model version 0 [2024-12-07 14:34:11,046][23806] LearnerWorker_p0 finished initialization! [2024-12-07 14:34:11,046][23806] Using GPUs [0] for process 0 (actually maps to GPUs [0]) [2024-12-07 14:34:11,160][23819] RunningMeanStd input shape: (3, 72, 128) [2024-12-07 14:34:11,160][23819] RunningMeanStd input shape: (1,) [2024-12-07 14:34:11,167][23819] ConvEncoder: input_channels=3 [2024-12-07 14:34:11,230][23819] Conv encoder output size: 512 [2024-12-07 14:34:11,230][23819] Policy head output size: 512 [2024-12-07 14:34:11,255][19013] Inference worker 0-0 is ready! [2024-12-07 14:34:11,256][19013] All inference workers are ready! Signal rollout workers to start! [2024-12-07 14:34:11,298][23820] Doom resolution: 160x120, resize resolution: (128, 72) [2024-12-07 14:34:11,298][23823] Doom resolution: 160x120, resize resolution: (128, 72) [2024-12-07 14:34:11,300][23821] Doom resolution: 160x120, resize resolution: (128, 72) [2024-12-07 14:34:11,300][23824] Doom resolution: 160x120, resize resolution: (128, 72) [2024-12-07 14:34:11,301][23822] Doom resolution: 160x120, resize resolution: (128, 72) [2024-12-07 14:34:11,301][23826] Doom resolution: 160x120, resize resolution: (128, 72) [2024-12-07 14:34:11,304][23825] Doom resolution: 160x120, resize resolution: (128, 72) [2024-12-07 14:34:11,306][23827] Doom resolution: 160x120, resize resolution: (128, 72) [2024-12-07 14:34:11,542][23824] Decorrelating experience for 0 frames... [2024-12-07 14:34:11,542][23826] Decorrelating experience for 0 frames... [2024-12-07 14:34:11,542][23820] Decorrelating experience for 0 frames... [2024-12-07 14:34:11,543][23827] Decorrelating experience for 0 frames... [2024-12-07 14:34:11,749][23827] Decorrelating experience for 32 frames... [2024-12-07 14:34:11,749][23824] Decorrelating experience for 32 frames... [2024-12-07 14:34:11,750][23826] Decorrelating experience for 32 frames... [2024-12-07 14:34:11,788][23825] Decorrelating experience for 0 frames... [2024-12-07 14:34:11,791][23823] Decorrelating experience for 0 frames... [2024-12-07 14:34:11,993][23825] Decorrelating experience for 32 frames... [2024-12-07 14:34:11,999][23823] Decorrelating experience for 32 frames... [2024-12-07 14:34:12,004][23827] Decorrelating experience for 64 frames... [2024-12-07 14:34:12,010][23826] Decorrelating experience for 64 frames... [2024-12-07 14:34:12,234][23827] Decorrelating experience for 96 frames... [2024-12-07 14:34:12,248][23825] Decorrelating experience for 64 frames... [2024-12-07 14:34:12,252][23823] Decorrelating experience for 64 frames... [2024-12-07 14:34:12,473][23825] Decorrelating experience for 96 frames... [2024-12-07 14:34:12,507][23824] Decorrelating experience for 64 frames... [2024-12-07 14:34:12,676][23823] Decorrelating experience for 96 frames... [2024-12-07 14:34:12,733][23824] Decorrelating experience for 96 frames... [2024-12-07 14:34:13,342][23806] Signal inference workers to stop experience collection... [2024-12-07 14:34:13,344][23819] InferenceWorker_p0-w0: stopping experience collection [2024-12-07 14:34:14,621][23806] Signal inference workers to resume experience collection... [2024-12-07 14:34:14,621][23819] InferenceWorker_p0-w0: resuming experience collection [2024-12-07 14:34:15,572][19013] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 20480. Throughput: 0: nan. Samples: 2290. Policy #0 lag: (min: 0.0, avg: 0.0, max: 0.0) [2024-12-07 14:34:15,573][19013] Avg episode reward: [(0, '3.977')] [2024-12-07 14:34:16,809][23819] Updated weights for policy 0, policy_version 10 (0.0055) [2024-12-07 14:34:19,427][23819] Updated weights for policy 0, policy_version 20 (0.0005) [2024-12-07 14:34:20,572][19013] Fps is (10 sec: 15564.9, 60 sec: 15564.9, 300 sec: 15564.9). Total num frames: 98304. Throughput: 0: 4348.4. Samples: 24032. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) [2024-12-07 14:34:20,573][19013] Avg episode reward: [(0, '4.353')] [2024-12-07 14:34:22,008][23819] Updated weights for policy 0, policy_version 30 (0.0005) [2024-12-07 14:34:24,540][23819] Updated weights for policy 0, policy_version 40 (0.0005) [2024-12-07 14:34:25,572][19013] Fps is (10 sec: 15564.9, 60 sec: 15564.9, 300 sec: 15564.9). Total num frames: 176128. Throughput: 0: 3386.4. Samples: 36154. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) [2024-12-07 14:34:25,573][19013] Avg episode reward: [(0, '4.445')] [2024-12-07 14:34:25,590][23806] Saving new best policy, reward=4.445! [2024-12-07 14:34:27,136][23819] Updated weights for policy 0, policy_version 50 (0.0005) [2024-12-07 14:34:28,436][19013] Heartbeat connected on Batcher_0 [2024-12-07 14:34:28,440][19013] Heartbeat connected on LearnerWorker_p0 [2024-12-07 14:34:28,446][19013] Heartbeat connected on InferenceWorker_p0-w0 [2024-12-07 14:34:28,458][19013] Heartbeat connected on RolloutWorker_w2 [2024-12-07 14:34:28,464][19013] Heartbeat connected on RolloutWorker_w4 [2024-12-07 14:34:28,467][19013] Heartbeat connected on RolloutWorker_w5 [2024-12-07 14:34:28,474][19013] Heartbeat connected on RolloutWorker_w6 [2024-12-07 14:34:29,728][23819] Updated weights for policy 0, policy_version 60 (0.0005) [2024-12-07 14:34:30,572][19013] Fps is (10 sec: 15974.3, 60 sec: 15837.9, 300 sec: 15837.9). Total num frames: 258048. Throughput: 0: 3843.9. Samples: 59948. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) [2024-12-07 14:34:30,573][19013] Avg episode reward: [(0, '4.400')] [2024-12-07 14:34:31,330][23822] Another process currently holds the lock /tmp/sf2_rahatchd/doom_003.lockfile, attempt: 1 [2024-12-07 14:34:31,588][23820] Another process currently holds the lock /tmp/sf2_rahatchd/doom_003.lockfile, attempt: 1 [2024-12-07 14:34:32,034][23826] Another process currently holds the lock /tmp/sf2_rahatchd/doom_003.lockfile, attempt: 1 [2024-12-07 14:34:32,279][23819] Updated weights for policy 0, policy_version 70 (0.0005) [2024-12-07 14:34:34,871][23819] Updated weights for policy 0, policy_version 80 (0.0005) [2024-12-07 14:34:35,572][19013] Fps is (10 sec: 15974.4, 60 sec: 15769.6, 300 sec: 15769.6). Total num frames: 335872. Throughput: 0: 4069.7. Samples: 83684. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) [2024-12-07 14:34:35,573][19013] Avg episode reward: [(0, '4.238')] [2024-12-07 14:34:37,529][23819] Updated weights for policy 0, policy_version 90 (0.0005) [2024-12-07 14:34:40,153][23819] Updated weights for policy 0, policy_version 100 (0.0005) [2024-12-07 14:34:40,258][23826] Decorrelating experience for 96 frames... [2024-12-07 14:34:40,299][19013] Heartbeat connected on RolloutWorker_w7 [2024-12-07 14:34:40,520][23822] Decorrelating experience for 0 frames... [2024-12-07 14:34:40,572][19013] Fps is (10 sec: 15564.9, 60 sec: 15728.7, 300 sec: 15728.7). Total num frames: 413696. Throughput: 0: 3723.6. Samples: 95380. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) [2024-12-07 14:34:40,573][19013] Avg episode reward: [(0, '4.623')] [2024-12-07 14:34:40,574][23806] Saving new best policy, reward=4.623! [2024-12-07 14:34:40,767][23820] Decorrelating experience for 32 frames... [2024-12-07 14:34:40,779][23822] Decorrelating experience for 32 frames... [2024-12-07 14:34:41,047][23820] Decorrelating experience for 64 frames... [2024-12-07 14:34:41,089][23822] Decorrelating experience for 64 frames... [2024-12-07 14:34:41,289][23820] Decorrelating experience for 96 frames... [2024-12-07 14:34:41,334][19013] Heartbeat connected on RolloutWorker_w0 [2024-12-07 14:34:41,366][23822] Decorrelating experience for 96 frames... [2024-12-07 14:34:41,413][19013] Heartbeat connected on RolloutWorker_w3 [2024-12-07 14:34:42,172][23819] Updated weights for policy 0, policy_version 110 (0.0006) [2024-12-07 14:34:43,880][23819] Updated weights for policy 0, policy_version 120 (0.0006) [2024-12-07 14:34:45,572][19013] Fps is (10 sec: 19251.0, 60 sec: 16930.1, 300 sec: 16930.1). Total num frames: 528384. Throughput: 0: 4111.3. Samples: 125628. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2024-12-07 14:34:45,573][19013] Avg episode reward: [(0, '4.533')] [2024-12-07 14:34:45,636][23819] Updated weights for policy 0, policy_version 130 (0.0006) [2024-12-07 14:34:47,368][23819] Updated weights for policy 0, policy_version 140 (0.0006) [2024-12-07 14:34:49,090][23819] Updated weights for policy 0, policy_version 150 (0.0006) [2024-12-07 14:34:50,572][19013] Fps is (10 sec: 23347.2, 60 sec: 17905.4, 300 sec: 17905.4). Total num frames: 647168. Throughput: 0: 4548.9. Samples: 161502. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) [2024-12-07 14:34:50,572][19013] Avg episode reward: [(0, '4.887')] [2024-12-07 14:34:50,599][23806] Saving new best policy, reward=4.887! [2024-12-07 14:34:50,759][23819] Updated weights for policy 0, policy_version 160 (0.0006) [2024-12-07 14:34:52,474][23819] Updated weights for policy 0, policy_version 170 (0.0006) [2024-12-07 14:34:54,205][23819] Updated weights for policy 0, policy_version 180 (0.0006) [2024-12-07 14:34:55,572][19013] Fps is (10 sec: 24166.6, 60 sec: 18739.2, 300 sec: 18739.2). Total num frames: 770048. Throughput: 0: 4428.4. Samples: 179426. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2024-12-07 14:34:55,573][19013] Avg episode reward: [(0, '5.630')] [2024-12-07 14:34:55,577][23806] Saving new best policy, reward=5.630! [2024-12-07 14:34:55,906][23819] Updated weights for policy 0, policy_version 190 (0.0006) [2024-12-07 14:34:57,612][23819] Updated weights for policy 0, policy_version 200 (0.0005) [2024-12-07 14:34:59,318][23819] Updated weights for policy 0, policy_version 210 (0.0005) [2024-12-07 14:35:00,572][19013] Fps is (10 sec: 24166.4, 60 sec: 19296.7, 300 sec: 19296.7). Total num frames: 888832. Throughput: 0: 4731.9. Samples: 215224. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2024-12-07 14:35:00,573][19013] Avg episode reward: [(0, '6.418')] [2024-12-07 14:35:00,574][23806] Saving new best policy, reward=6.418! [2024-12-07 14:35:01,062][23819] Updated weights for policy 0, policy_version 220 (0.0005) [2024-12-07 14:35:02,771][23819] Updated weights for policy 0, policy_version 230 (0.0005) [2024-12-07 14:35:04,449][23819] Updated weights for policy 0, policy_version 240 (0.0006) [2024-12-07 14:35:05,572][19013] Fps is (10 sec: 23756.6, 60 sec: 19742.7, 300 sec: 19742.7). Total num frames: 1007616. Throughput: 0: 5050.4. Samples: 251302. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2024-12-07 14:35:05,573][19013] Avg episode reward: [(0, '6.368')] [2024-12-07 14:35:06,143][23819] Updated weights for policy 0, policy_version 250 (0.0005) [2024-12-07 14:35:07,805][23819] Updated weights for policy 0, policy_version 260 (0.0005) [2024-12-07 14:35:09,486][23819] Updated weights for policy 0, policy_version 270 (0.0006) [2024-12-07 14:35:10,572][19013] Fps is (10 sec: 24166.5, 60 sec: 20182.1, 300 sec: 20182.1). Total num frames: 1130496. Throughput: 0: 5188.9. Samples: 269656. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2024-12-07 14:35:10,572][19013] Avg episode reward: [(0, '6.861')] [2024-12-07 14:35:10,574][23806] Saving new best policy, reward=6.861! [2024-12-07 14:35:11,214][23819] Updated weights for policy 0, policy_version 280 (0.0006) [2024-12-07 14:35:12,978][23819] Updated weights for policy 0, policy_version 290 (0.0006) [2024-12-07 14:35:14,732][23819] Updated weights for policy 0, policy_version 300 (0.0005) [2024-12-07 14:35:15,572][19013] Fps is (10 sec: 23756.7, 60 sec: 20411.7, 300 sec: 20411.7). Total num frames: 1245184. Throughput: 0: 5446.4. Samples: 305038. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2024-12-07 14:35:15,573][19013] Avg episode reward: [(0, '10.907')] [2024-12-07 14:35:15,601][23806] Saving new best policy, reward=10.907! [2024-12-07 14:35:16,460][23819] Updated weights for policy 0, policy_version 310 (0.0006) [2024-12-07 14:35:18,153][23819] Updated weights for policy 0, policy_version 320 (0.0005) [2024-12-07 14:35:19,869][23819] Updated weights for policy 0, policy_version 330 (0.0006) [2024-12-07 14:35:20,572][19013] Fps is (10 sec: 23756.7, 60 sec: 21162.7, 300 sec: 20732.1). Total num frames: 1368064. Throughput: 0: 5715.3. Samples: 340872. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2024-12-07 14:35:20,573][19013] Avg episode reward: [(0, '10.922')] [2024-12-07 14:35:20,574][23806] Saving new best policy, reward=10.922! [2024-12-07 14:35:21,588][23819] Updated weights for policy 0, policy_version 340 (0.0005) [2024-12-07 14:35:23,313][23819] Updated weights for policy 0, policy_version 350 (0.0006) [2024-12-07 14:35:24,968][23819] Updated weights for policy 0, policy_version 360 (0.0005) [2024-12-07 14:35:25,572][19013] Fps is (10 sec: 24166.6, 60 sec: 21845.3, 300 sec: 20948.1). Total num frames: 1486848. Throughput: 0: 5854.0. Samples: 358812. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2024-12-07 14:35:25,573][19013] Avg episode reward: [(0, '14.171')] [2024-12-07 14:35:25,576][23806] Saving new best policy, reward=14.171! [2024-12-07 14:35:26,667][23819] Updated weights for policy 0, policy_version 370 (0.0006) [2024-12-07 14:35:28,436][23819] Updated weights for policy 0, policy_version 380 (0.0006) [2024-12-07 14:35:30,136][23819] Updated weights for policy 0, policy_version 390 (0.0006) [2024-12-07 14:35:30,572][19013] Fps is (10 sec: 23756.7, 60 sec: 22459.7, 300 sec: 21135.3). Total num frames: 1605632. Throughput: 0: 5982.7. Samples: 394850. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2024-12-07 14:35:30,573][19013] Avg episode reward: [(0, '15.187')] [2024-12-07 14:35:30,574][23806] Saving new best policy, reward=15.187! [2024-12-07 14:35:31,846][23819] Updated weights for policy 0, policy_version 400 (0.0006) [2024-12-07 14:35:33,531][23819] Updated weights for policy 0, policy_version 410 (0.0006) [2024-12-07 14:35:35,243][23819] Updated weights for policy 0, policy_version 420 (0.0006) [2024-12-07 14:35:35,572][19013] Fps is (10 sec: 23756.8, 60 sec: 23142.4, 300 sec: 21299.2). Total num frames: 1724416. Throughput: 0: 5984.0. Samples: 430784. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) [2024-12-07 14:35:35,573][19013] Avg episode reward: [(0, '17.358')] [2024-12-07 14:35:35,580][23806] Saving new best policy, reward=17.358! [2024-12-07 14:35:36,966][23819] Updated weights for policy 0, policy_version 430 (0.0006) [2024-12-07 14:35:38,650][23819] Updated weights for policy 0, policy_version 440 (0.0006) [2024-12-07 14:35:40,348][23819] Updated weights for policy 0, policy_version 450 (0.0006) [2024-12-07 14:35:40,572][19013] Fps is (10 sec: 24166.6, 60 sec: 23893.3, 300 sec: 21492.0). Total num frames: 1847296. Throughput: 0: 5988.3. Samples: 448900. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2024-12-07 14:35:40,573][19013] Avg episode reward: [(0, '14.961')] [2024-12-07 14:35:42,052][23819] Updated weights for policy 0, policy_version 460 (0.0006) [2024-12-07 14:35:43,761][23819] Updated weights for policy 0, policy_version 470 (0.0006) [2024-12-07 14:35:45,477][23819] Updated weights for policy 0, policy_version 480 (0.0005) [2024-12-07 14:35:45,572][19013] Fps is (10 sec: 24166.5, 60 sec: 23961.6, 300 sec: 21617.8). Total num frames: 1966080. Throughput: 0: 5992.3. Samples: 484878. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2024-12-07 14:35:45,572][19013] Avg episode reward: [(0, '17.778')] [2024-12-07 14:35:45,574][23806] Saving new best policy, reward=17.778! [2024-12-07 14:35:47,212][23819] Updated weights for policy 0, policy_version 490 (0.0006) [2024-12-07 14:35:48,912][23819] Updated weights for policy 0, policy_version 500 (0.0006) [2024-12-07 14:35:50,572][19013] Fps is (10 sec: 23756.7, 60 sec: 23961.6, 300 sec: 21730.4). Total num frames: 2084864. Throughput: 0: 5987.8. Samples: 520754. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2024-12-07 14:35:50,573][19013] Avg episode reward: [(0, '17.858')] [2024-12-07 14:35:50,593][23806] Saving new best policy, reward=17.858! [2024-12-07 14:35:50,593][23819] Updated weights for policy 0, policy_version 510 (0.0005) [2024-12-07 14:35:52,338][23819] Updated weights for policy 0, policy_version 520 (0.0006) [2024-12-07 14:35:54,023][23819] Updated weights for policy 0, policy_version 530 (0.0005) [2024-12-07 14:35:55,572][19013] Fps is (10 sec: 24166.2, 60 sec: 23961.6, 300 sec: 21872.6). Total num frames: 2207744. Throughput: 0: 5980.4. Samples: 538776. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) [2024-12-07 14:35:55,573][19013] Avg episode reward: [(0, '18.651')] [2024-12-07 14:35:55,577][23806] Saving new best policy, reward=18.651! [2024-12-07 14:35:55,691][23819] Updated weights for policy 0, policy_version 540 (0.0005) [2024-12-07 14:35:57,355][23819] Updated weights for policy 0, policy_version 550 (0.0005) [2024-12-07 14:35:59,071][23819] Updated weights for policy 0, policy_version 560 (0.0006) [2024-12-07 14:36:00,572][19013] Fps is (10 sec: 24166.4, 60 sec: 23961.6, 300 sec: 21962.4). Total num frames: 2326528. Throughput: 0: 6000.2. Samples: 575048. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2024-12-07 14:36:00,573][19013] Avg episode reward: [(0, '20.626')] [2024-12-07 14:36:00,574][23806] Saving new best policy, reward=20.626! [2024-12-07 14:36:00,803][23819] Updated weights for policy 0, policy_version 570 (0.0005) [2024-12-07 14:36:02,587][23819] Updated weights for policy 0, policy_version 580 (0.0006) [2024-12-07 14:36:04,280][23819] Updated weights for policy 0, policy_version 590 (0.0006) [2024-12-07 14:36:05,572][19013] Fps is (10 sec: 23756.9, 60 sec: 23961.6, 300 sec: 22043.9). Total num frames: 2445312. Throughput: 0: 5993.6. Samples: 610586. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) [2024-12-07 14:36:05,573][19013] Avg episode reward: [(0, '19.659')] [2024-12-07 14:36:05,578][23806] Saving /home/rahatchd/code/deep-rl-hugging-face/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000597_2445312.pth... [2024-12-07 14:36:06,010][23819] Updated weights for policy 0, policy_version 600 (0.0006) [2024-12-07 14:36:07,722][23819] Updated weights for policy 0, policy_version 610 (0.0006) [2024-12-07 14:36:09,485][23819] Updated weights for policy 0, policy_version 620 (0.0006) [2024-12-07 14:36:10,572][19013] Fps is (10 sec: 23756.8, 60 sec: 23893.3, 300 sec: 22118.4). Total num frames: 2564096. Throughput: 0: 5989.9. Samples: 628358. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) [2024-12-07 14:36:10,573][19013] Avg episode reward: [(0, '20.821')] [2024-12-07 14:36:10,573][23806] Saving new best policy, reward=20.821! [2024-12-07 14:36:11,223][23819] Updated weights for policy 0, policy_version 630 (0.0006) [2024-12-07 14:36:12,900][23819] Updated weights for policy 0, policy_version 640 (0.0005) [2024-12-07 14:36:14,589][23819] Updated weights for policy 0, policy_version 650 (0.0005) [2024-12-07 14:36:15,572][19013] Fps is (10 sec: 23756.7, 60 sec: 23961.6, 300 sec: 22186.7). Total num frames: 2682880. Throughput: 0: 5983.3. Samples: 664100. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) [2024-12-07 14:36:15,573][19013] Avg episode reward: [(0, '25.818')] [2024-12-07 14:36:15,599][23806] Saving new best policy, reward=25.818! [2024-12-07 14:36:16,313][23819] Updated weights for policy 0, policy_version 660 (0.0006) [2024-12-07 14:36:18,010][23819] Updated weights for policy 0, policy_version 670 (0.0006) [2024-12-07 14:36:19,721][23819] Updated weights for policy 0, policy_version 680 (0.0006) [2024-12-07 14:36:20,572][19013] Fps is (10 sec: 23756.9, 60 sec: 23893.4, 300 sec: 22249.5). Total num frames: 2801664. Throughput: 0: 5985.2. Samples: 700118. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2024-12-07 14:36:20,573][19013] Avg episode reward: [(0, '23.182')] [2024-12-07 14:36:21,452][23819] Updated weights for policy 0, policy_version 690 (0.0006) [2024-12-07 14:36:23,178][23819] Updated weights for policy 0, policy_version 700 (0.0006) [2024-12-07 14:36:24,922][23819] Updated weights for policy 0, policy_version 710 (0.0006) [2024-12-07 14:36:25,572][19013] Fps is (10 sec: 24166.6, 60 sec: 23961.6, 300 sec: 22339.0). Total num frames: 2924544. Throughput: 0: 5977.3. Samples: 717878. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) [2024-12-07 14:36:25,572][19013] Avg episode reward: [(0, '25.883')] [2024-12-07 14:36:25,577][23806] Saving new best policy, reward=25.883! [2024-12-07 14:36:26,623][23819] Updated weights for policy 0, policy_version 720 (0.0006) [2024-12-07 14:36:28,373][23819] Updated weights for policy 0, policy_version 730 (0.0006) [2024-12-07 14:36:30,077][23819] Updated weights for policy 0, policy_version 740 (0.0006) [2024-12-07 14:36:30,572][19013] Fps is (10 sec: 24166.4, 60 sec: 23961.6, 300 sec: 22391.5). Total num frames: 3043328. Throughput: 0: 5966.4. Samples: 753368. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2024-12-07 14:36:30,573][19013] Avg episode reward: [(0, '24.200')] [2024-12-07 14:36:31,784][23819] Updated weights for policy 0, policy_version 750 (0.0006) [2024-12-07 14:36:33,524][23819] Updated weights for policy 0, policy_version 760 (0.0006) [2024-12-07 14:36:35,223][23819] Updated weights for policy 0, policy_version 770 (0.0006) [2024-12-07 14:36:35,572][19013] Fps is (10 sec: 23346.9, 60 sec: 23893.3, 300 sec: 22411.0). Total num frames: 3158016. Throughput: 0: 5966.9. Samples: 789264. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2024-12-07 14:36:35,573][19013] Avg episode reward: [(0, '27.345')] [2024-12-07 14:36:35,584][23806] Saving new best policy, reward=27.345! [2024-12-07 14:36:36,970][23819] Updated weights for policy 0, policy_version 780 (0.0006) [2024-12-07 14:36:38,637][23819] Updated weights for policy 0, policy_version 790 (0.0005) [2024-12-07 14:36:40,311][23819] Updated weights for policy 0, policy_version 800 (0.0005) [2024-12-07 14:36:40,572][19013] Fps is (10 sec: 23756.6, 60 sec: 23893.3, 300 sec: 22485.6). Total num frames: 3280896. Throughput: 0: 5969.2. Samples: 807390. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) [2024-12-07 14:36:40,573][19013] Avg episode reward: [(0, '24.428')] [2024-12-07 14:36:41,986][23819] Updated weights for policy 0, policy_version 810 (0.0005) [2024-12-07 14:36:43,711][23819] Updated weights for policy 0, policy_version 820 (0.0006) [2024-12-07 14:36:45,438][23819] Updated weights for policy 0, policy_version 830 (0.0006) [2024-12-07 14:36:45,572][19013] Fps is (10 sec: 24166.6, 60 sec: 23893.3, 300 sec: 22528.0). Total num frames: 3399680. Throughput: 0: 5968.0. Samples: 843610. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) [2024-12-07 14:36:45,573][19013] Avg episode reward: [(0, '26.352')] [2024-12-07 14:36:47,139][23819] Updated weights for policy 0, policy_version 840 (0.0006) [2024-12-07 14:36:48,865][23819] Updated weights for policy 0, policy_version 850 (0.0006) [2024-12-07 14:36:50,541][23819] Updated weights for policy 0, policy_version 860 (0.0006) [2024-12-07 14:36:50,572][19013] Fps is (10 sec: 24166.3, 60 sec: 23961.6, 300 sec: 22594.1). Total num frames: 3522560. Throughput: 0: 5975.9. Samples: 879504. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2024-12-07 14:36:50,573][19013] Avg episode reward: [(0, '25.960')] [2024-12-07 14:36:52,301][23819] Updated weights for policy 0, policy_version 870 (0.0005) [2024-12-07 14:36:54,003][23819] Updated weights for policy 0, policy_version 880 (0.0006) [2024-12-07 14:36:55,572][19013] Fps is (10 sec: 24166.6, 60 sec: 23893.4, 300 sec: 22630.4). Total num frames: 3641344. Throughput: 0: 5978.9. Samples: 897410. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) [2024-12-07 14:36:55,572][19013] Avg episode reward: [(0, '26.897')] [2024-12-07 14:36:55,723][23819] Updated weights for policy 0, policy_version 890 (0.0006) [2024-12-07 14:36:57,435][23819] Updated weights for policy 0, policy_version 900 (0.0006) [2024-12-07 14:36:59,172][23819] Updated weights for policy 0, policy_version 910 (0.0005) [2024-12-07 14:37:00,572][19013] Fps is (10 sec: 23756.8, 60 sec: 23893.3, 300 sec: 22664.5). Total num frames: 3760128. Throughput: 0: 5978.4. Samples: 933130. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0) [2024-12-07 14:37:00,573][19013] Avg episode reward: [(0, '25.898')] [2024-12-07 14:37:00,844][23819] Updated weights for policy 0, policy_version 920 (0.0005) [2024-12-07 14:37:02,559][23819] Updated weights for policy 0, policy_version 930 (0.0006) [2024-12-07 14:37:04,287][23819] Updated weights for policy 0, policy_version 940 (0.0006) [2024-12-07 14:37:05,572][19013] Fps is (10 sec: 23756.4, 60 sec: 23893.3, 300 sec: 22696.6). Total num frames: 3878912. Throughput: 0: 5973.1. Samples: 968908. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) [2024-12-07 14:37:05,573][19013] Avg episode reward: [(0, '26.120')] [2024-12-07 14:37:06,038][23819] Updated weights for policy 0, policy_version 950 (0.0006) [2024-12-07 14:37:07,756][23819] Updated weights for policy 0, policy_version 960 (0.0006) [2024-12-07 14:37:09,463][23819] Updated weights for policy 0, policy_version 970 (0.0005) [2024-12-07 14:37:10,572][19013] Fps is (10 sec: 23757.1, 60 sec: 23893.4, 300 sec: 22727.0). Total num frames: 3997696. Throughput: 0: 5973.8. Samples: 986698. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) [2024-12-07 14:37:10,573][19013] Avg episode reward: [(0, '25.009')] [2024-12-07 14:37:10,814][23806] Stopping Batcher_0... [2024-12-07 14:37:10,814][23806] Saving /home/rahatchd/code/deep-rl-hugging-face/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... [2024-12-07 14:37:10,814][19013] Component Batcher_0 stopped! [2024-12-07 14:37:10,816][19013] Component RolloutWorker_w1 process died already! Don't wait for it. [2024-12-07 14:37:10,815][23806] Loop batcher_evt_loop terminating... [2024-12-07 14:37:10,832][23819] Weights refcount: 2 0 [2024-12-07 14:37:10,833][23819] Stopping InferenceWorker_p0-w0... [2024-12-07 14:37:10,833][23819] Loop inference_proc0-0_evt_loop terminating... [2024-12-07 14:37:10,833][19013] Component InferenceWorker_p0-w0 stopped! [2024-12-07 14:37:10,849][23824] Stopping RolloutWorker_w5... [2024-12-07 14:37:10,850][23824] Loop rollout_proc5_evt_loop terminating... [2024-12-07 14:37:10,849][19013] Component RolloutWorker_w5 stopped! [2024-12-07 14:37:10,851][23823] Stopping RolloutWorker_w4... [2024-12-07 14:37:10,851][19013] Component RolloutWorker_w4 stopped! [2024-12-07 14:37:10,851][23820] Stopping RolloutWorker_w0... [2024-12-07 14:37:10,851][23823] Loop rollout_proc4_evt_loop terminating... [2024-12-07 14:37:10,851][23820] Loop rollout_proc0_evt_loop terminating... [2024-12-07 14:37:10,851][19013] Component RolloutWorker_w0 stopped! [2024-12-07 14:37:10,851][23826] Stopping RolloutWorker_w7... [2024-12-07 14:37:10,852][23826] Loop rollout_proc7_evt_loop terminating... [2024-12-07 14:37:10,852][19013] Component RolloutWorker_w7 stopped! [2024-12-07 14:37:10,852][23827] Stopping RolloutWorker_w6... [2024-12-07 14:37:10,852][23822] Stopping RolloutWorker_w3... [2024-12-07 14:37:10,853][23822] Loop rollout_proc3_evt_loop terminating... [2024-12-07 14:37:10,853][23827] Loop rollout_proc6_evt_loop terminating... [2024-12-07 14:37:10,852][19013] Component RolloutWorker_w6 stopped! [2024-12-07 14:37:10,853][19013] Component RolloutWorker_w3 stopped! [2024-12-07 14:37:10,854][23825] Stopping RolloutWorker_w2... [2024-12-07 14:37:10,854][23825] Loop rollout_proc2_evt_loop terminating... [2024-12-07 14:37:10,854][19013] Component RolloutWorker_w2 stopped! [2024-12-07 14:37:10,869][23806] Saving /home/rahatchd/code/deep-rl-hugging-face/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... [2024-12-07 14:37:10,939][23806] Stopping LearnerWorker_p0... [2024-12-07 14:37:10,939][23806] Loop learner_proc0_evt_loop terminating... [2024-12-07 14:37:10,939][19013] Component LearnerWorker_p0 stopped! [2024-12-07 14:37:10,940][19013] Waiting for process learner_proc0 to stop... [2024-12-07 14:37:11,717][19013] Waiting for process inference_proc0-0 to join... [2024-12-07 14:37:11,718][19013] Waiting for process rollout_proc0 to join... [2024-12-07 14:37:11,718][19013] Waiting for process rollout_proc1 to join... [2024-12-07 14:37:11,719][19013] Waiting for process rollout_proc2 to join... [2024-12-07 14:37:11,720][19013] Waiting for process rollout_proc3 to join... [2024-12-07 14:37:11,721][19013] Waiting for process rollout_proc4 to join... [2024-12-07 14:37:11,722][19013] Waiting for process rollout_proc5 to join... [2024-12-07 14:37:11,723][19013] Waiting for process rollout_proc6 to join... [2024-12-07 14:37:11,724][19013] Waiting for process rollout_proc7 to join... [2024-12-07 14:37:11,725][19013] Batcher 0 profile tree view: batching: 10.5578, releasing_batches: 0.0212 [2024-12-07 14:37:11,726][19013] InferenceWorker_p0-w0 profile tree view: wait_policy: 0.0000 wait_policy_total: 4.8273 update_model: 2.0921 weight_update: 0.0005 one_step: 0.0017 handle_policy_step: 163.6695 deserialize: 5.4997, stack: 0.7929, obs_to_device_normalize: 41.8845, forward: 72.2956, send_messages: 7.4725 prepare_outputs: 29.4647 to_cpu: 21.6545 [2024-12-07 14:37:11,726][19013] Learner 0 profile tree view: misc: 0.0039, prepare_batch: 7.2401 train: 26.4145 epoch_init: 0.0037, minibatch_init: 0.0042, losses_postprocess: 0.3988, kl_divergence: 0.3982, after_optimizer: 9.2403 calculate_losses: 9.3937 losses_init: 0.0027, forward_head: 0.5595, bptt_initial: 6.1467, tail: 0.4752, advantages_returns: 0.1337, losses: 0.9484 bptt: 0.9985 bptt_forward_core: 0.9580 update: 6.6839 clip: 0.6204 [2024-12-07 14:37:11,727][19013] RolloutWorker_w0 profile tree view: wait_for_trajectories: 0.1300, enqueue_policy_requests: 8.3049, env_step: 101.9303, overhead: 4.5937, complete_rollouts: 0.1726 save_policy_outputs: 8.8612 split_output_tensors: 2.9795 [2024-12-07 14:37:11,728][19013] RolloutWorker_w7 profile tree view: wait_for_trajectories: 0.1243, enqueue_policy_requests: 8.2736, env_step: 100.7709, overhead: 4.5028, complete_rollouts: 0.1745 save_policy_outputs: 8.7132 split_output_tensors: 2.9265 [2024-12-07 14:37:11,729][19013] Loop Runner_EvtLoop terminating... [2024-12-07 14:37:11,730][19013] Runner profile tree view: main_loop: 183.2567 [2024-12-07 14:37:11,732][19013] Collected {0: 4005888}, FPS: 21859.4 [2024-12-07 14:39:41,500][19013] Loading existing experiment configuration from /home/rahatchd/code/deep-rl-hugging-face/unit8/train_dir/default_experiment/config.json [2024-12-07 14:39:41,500][19013] Overriding arg 'num_workers' with value 1 passed from command line [2024-12-07 14:39:41,501][19013] Adding new argument 'no_render'=True that is not in the saved config file! [2024-12-07 14:39:41,501][19013] Adding new argument 'save_video'=True that is not in the saved config file! [2024-12-07 14:39:41,502][19013] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! [2024-12-07 14:39:41,502][19013] Adding new argument 'video_name'=None that is not in the saved config file! [2024-12-07 14:39:41,503][19013] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file! [2024-12-07 14:39:41,504][19013] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! [2024-12-07 14:39:41,504][19013] Adding new argument 'push_to_hub'=False that is not in the saved config file! [2024-12-07 14:39:41,504][19013] Adding new argument 'hf_repository'=None that is not in the saved config file! [2024-12-07 14:39:41,505][19013] Adding new argument 'policy_index'=0 that is not in the saved config file! [2024-12-07 14:39:41,505][19013] Adding new argument 'eval_deterministic'=False that is not in the saved config file! [2024-12-07 14:39:41,505][19013] Adding new argument 'train_script'=None that is not in the saved config file! [2024-12-07 14:39:41,506][19013] Adding new argument 'enjoy_script'=None that is not in the saved config file! [2024-12-07 14:39:41,506][19013] Using frameskip 1 and render_action_repeat=4 for evaluation [2024-12-07 14:39:41,526][19013] Doom resolution: 160x120, resize resolution: (128, 72) [2024-12-07 14:39:41,528][19013] RunningMeanStd input shape: (3, 72, 128) [2024-12-07 14:39:41,529][19013] RunningMeanStd input shape: (1,) [2024-12-07 14:39:41,535][19013] ConvEncoder: input_channels=3 [2024-12-07 14:39:41,599][19013] Conv encoder output size: 512 [2024-12-07 14:39:41,599][19013] Policy head output size: 512 [2024-12-07 14:39:41,735][19013] Loading state from checkpoint /home/rahatchd/code/deep-rl-hugging-face/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... [2024-12-07 14:39:42,277][19013] Num frames 100... [2024-12-07 14:39:42,353][19013] Num frames 200... [2024-12-07 14:39:42,433][19013] Num frames 300... [2024-12-07 14:39:42,515][19013] Num frames 400... [2024-12-07 14:39:42,589][19013] Num frames 500... [2024-12-07 14:39:42,665][19013] Num frames 600... [2024-12-07 14:39:42,741][19013] Num frames 700... [2024-12-07 14:39:42,823][19013] Num frames 800... [2024-12-07 14:39:42,906][19013] Num frames 900... [2024-12-07 14:39:43,035][19013] Avg episode rewards: #0: 19.920, true rewards: #0: 9.920 [2024-12-07 14:39:43,036][19013] Avg episode reward: 19.920, avg true_objective: 9.920 [2024-12-07 14:39:43,045][19013] Num frames 1000... [2024-12-07 14:39:43,164][19013] Num frames 1100... [2024-12-07 14:39:43,256][19013] Num frames 1200... [2024-12-07 14:39:43,336][19013] Num frames 1300... [2024-12-07 14:39:43,419][19013] Num frames 1400... [2024-12-07 14:39:43,499][19013] Num frames 1500... [2024-12-07 14:39:43,586][19013] Num frames 1600... [2024-12-07 14:39:43,674][19013] Num frames 1700... [2024-12-07 14:39:43,760][19013] Num frames 1800... [2024-12-07 14:39:43,842][19013] Num frames 1900... [2024-12-07 14:39:43,924][19013] Num frames 2000... [2024-12-07 14:39:44,007][19013] Num frames 2100... [2024-12-07 14:39:44,104][19013] Avg episode rewards: #0: 22.220, true rewards: #0: 10.720 [2024-12-07 14:39:44,105][19013] Avg episode reward: 22.220, avg true_objective: 10.720 [2024-12-07 14:39:44,179][19013] Num frames 2200... [2024-12-07 14:39:44,285][19013] Num frames 2300... [2024-12-07 14:39:44,370][19013] Num frames 2400... [2024-12-07 14:39:44,450][19013] Num frames 2500... [2024-12-07 14:39:44,532][19013] Num frames 2600... [2024-12-07 14:39:44,613][19013] Num frames 2700... [2024-12-07 14:39:44,694][19013] Num frames 2800... [2024-12-07 14:39:44,778][19013] Num frames 2900... [2024-12-07 14:39:44,859][19013] Num frames 3000... [2024-12-07 14:39:44,941][19013] Num frames 3100... [2024-12-07 14:39:45,018][19013] Num frames 3200... [2024-12-07 14:39:45,085][19013] Num frames 3300... [2024-12-07 14:39:45,154][19013] Num frames 3400... [2024-12-07 14:39:45,222][19013] Num frames 3500... [2024-12-07 14:39:45,292][19013] Num frames 3600... [2024-12-07 14:39:45,360][19013] Num frames 3700... [2024-12-07 14:39:45,430][19013] Num frames 3800... [2024-12-07 14:39:45,500][19013] Num frames 3900... [2024-12-07 14:39:45,569][19013] Num frames 4000... [2024-12-07 14:39:45,640][19013] Num frames 4100... [2024-12-07 14:39:45,710][19013] Num frames 4200... [2024-12-07 14:39:45,794][19013] Avg episode rewards: #0: 35.480, true rewards: #0: 14.147 [2024-12-07 14:39:45,794][19013] Avg episode reward: 35.480, avg true_objective: 14.147 [2024-12-07 14:39:45,861][19013] Num frames 4300... [2024-12-07 14:39:45,944][19013] Num frames 4400... [2024-12-07 14:39:46,013][19013] Num frames 4500... [2024-12-07 14:39:46,083][19013] Num frames 4600... [2024-12-07 14:39:46,152][19013] Num frames 4700... [2024-12-07 14:39:46,222][19013] Num frames 4800... [2024-12-07 14:39:46,291][19013] Num frames 4900... [2024-12-07 14:39:46,361][19013] Num frames 5000... [2024-12-07 14:39:46,430][19013] Num frames 5100... [2024-12-07 14:39:46,519][19013] Avg episode rewards: #0: 32.100, true rewards: #0: 12.850 [2024-12-07 14:39:46,520][19013] Avg episode reward: 32.100, avg true_objective: 12.850 [2024-12-07 14:39:46,584][19013] Num frames 5200... [2024-12-07 14:39:46,691][19013] Num frames 5300... [2024-12-07 14:39:46,770][19013] Num frames 5400... [2024-12-07 14:39:46,852][19013] Num frames 5500... [2024-12-07 14:39:46,979][19013] Avg episode rewards: #0: 27.376, true rewards: #0: 11.176 [2024-12-07 14:39:46,980][19013] Avg episode reward: 27.376, avg true_objective: 11.176 [2024-12-07 14:39:46,996][19013] Num frames 5600... [2024-12-07 14:39:47,115][19013] Num frames 5700... [2024-12-07 14:39:47,210][19013] Num frames 5800... [2024-12-07 14:39:47,292][19013] Num frames 5900... [2024-12-07 14:39:47,373][19013] Num frames 6000... [2024-12-07 14:39:47,455][19013] Num frames 6100... [2024-12-07 14:39:47,535][19013] Num frames 6200... [2024-12-07 14:39:47,615][19013] Num frames 6300... [2024-12-07 14:39:47,697][19013] Num frames 6400... [2024-12-07 14:39:47,793][19013] Avg episode rewards: #0: 25.587, true rewards: #0: 10.753 [2024-12-07 14:39:47,794][19013] Avg episode reward: 25.587, avg true_objective: 10.753 [2024-12-07 14:39:47,853][19013] Num frames 6500... [2024-12-07 14:39:47,966][19013] Num frames 6600... [2024-12-07 14:39:48,057][19013] Num frames 6700... [2024-12-07 14:39:48,137][19013] Num frames 6800... [2024-12-07 14:39:48,216][19013] Num frames 6900... [2024-12-07 14:39:48,292][19013] Num frames 7000... [2024-12-07 14:39:48,385][19013] Avg episode rewards: #0: 23.228, true rewards: #0: 10.086 [2024-12-07 14:39:48,386][19013] Avg episode reward: 23.228, avg true_objective: 10.086 [2024-12-07 14:39:48,438][19013] Num frames 7100... [2024-12-07 14:39:48,539][19013] Num frames 7200... [2024-12-07 14:39:48,609][19013] Num frames 7300... [2024-12-07 14:39:48,679][19013] Num frames 7400... [2024-12-07 14:39:48,749][19013] Num frames 7500... [2024-12-07 14:39:48,820][19013] Num frames 7600... [2024-12-07 14:39:48,897][19013] Avg episode rewards: #0: 21.545, true rewards: #0: 9.545 [2024-12-07 14:39:48,898][19013] Avg episode reward: 21.545, avg true_objective: 9.545 [2024-12-07 14:39:48,972][19013] Num frames 7700... [2024-12-07 14:39:49,056][19013] Num frames 7800... [2024-12-07 14:39:49,125][19013] Num frames 7900... [2024-12-07 14:39:49,194][19013] Num frames 8000... [2024-12-07 14:39:49,284][19013] Avg episode rewards: #0: 19.949, true rewards: #0: 8.949 [2024-12-07 14:39:49,284][19013] Avg episode reward: 19.949, avg true_objective: 8.949 [2024-12-07 14:39:49,342][19013] Num frames 8100... [2024-12-07 14:39:49,431][19013] Num frames 8200... [2024-12-07 14:39:49,502][19013] Num frames 8300... [2024-12-07 14:39:49,572][19013] Num frames 8400... [2024-12-07 14:39:49,641][19013] Num frames 8500... [2024-12-07 14:39:49,711][19013] Num frames 8600... [2024-12-07 14:39:49,782][19013] Num frames 8700... [2024-12-07 14:39:49,864][19013] Avg episode rewards: #0: 19.336, true rewards: #0: 8.736 [2024-12-07 14:39:49,865][19013] Avg episode reward: 19.336, avg true_objective: 8.736 [2024-12-07 14:40:01,237][19013] Replay video saved to /home/rahatchd/code/deep-rl-hugging-face/unit8/train_dir/default_experiment/replay.mp4! [2024-12-07 14:41:09,726][19013] Loading existing experiment configuration from /home/rahatchd/code/deep-rl-hugging-face/unit8/train_dir/default_experiment/config.json [2024-12-07 14:41:09,727][19013] Overriding arg 'num_workers' with value 1 passed from command line [2024-12-07 14:41:09,727][19013] Adding new argument 'no_render'=True that is not in the saved config file! [2024-12-07 14:41:09,728][19013] Adding new argument 'save_video'=True that is not in the saved config file! [2024-12-07 14:41:09,728][19013] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! [2024-12-07 14:41:09,729][19013] Adding new argument 'video_name'=None that is not in the saved config file! [2024-12-07 14:41:09,730][19013] Adding new argument 'max_num_frames'=100000 that is not in the saved config file! [2024-12-07 14:41:09,730][19013] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! [2024-12-07 14:41:09,731][19013] Adding new argument 'push_to_hub'=True that is not in the saved config file! [2024-12-07 14:41:09,731][19013] Adding new argument 'hf_repository'='rahatchd/rl_course_vizdoom_health_gathering_supreme' that is not in the saved config file! [2024-12-07 14:41:09,731][19013] Adding new argument 'policy_index'=0 that is not in the saved config file! [2024-12-07 14:41:09,732][19013] Adding new argument 'eval_deterministic'=False that is not in the saved config file! [2024-12-07 14:41:09,732][19013] Adding new argument 'train_script'=None that is not in the saved config file! [2024-12-07 14:41:09,732][19013] Adding new argument 'enjoy_script'=None that is not in the saved config file! [2024-12-07 14:41:09,733][19013] Using frameskip 1 and render_action_repeat=4 for evaluation [2024-12-07 14:41:09,758][19013] RunningMeanStd input shape: (3, 72, 128) [2024-12-07 14:41:09,759][19013] RunningMeanStd input shape: (1,) [2024-12-07 14:41:09,769][19013] ConvEncoder: input_channels=3 [2024-12-07 14:41:09,803][19013] Conv encoder output size: 512 [2024-12-07 14:41:09,804][19013] Policy head output size: 512 [2024-12-07 14:41:09,836][19013] Loading state from checkpoint /home/rahatchd/code/deep-rl-hugging-face/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... [2024-12-07 14:41:10,172][19013] Num frames 100... [2024-12-07 14:41:10,256][19013] Num frames 200... [2024-12-07 14:41:10,340][19013] Num frames 300... [2024-12-07 14:41:10,423][19013] Num frames 400... [2024-12-07 14:41:10,506][19013] Num frames 500... [2024-12-07 14:41:10,589][19013] Num frames 600... [2024-12-07 14:41:10,676][19013] Num frames 700... [2024-12-07 14:41:10,758][19013] Num frames 800... [2024-12-07 14:41:10,843][19013] Num frames 900... [2024-12-07 14:41:10,926][19013] Num frames 1000... [2024-12-07 14:41:11,000][19013] Avg episode rewards: #0: 24.240, true rewards: #0: 10.240 [2024-12-07 14:41:11,000][19013] Avg episode reward: 24.240, avg true_objective: 10.240 [2024-12-07 14:41:11,065][19013] Num frames 1100... [2024-12-07 14:41:11,148][19013] Num frames 1200... [2024-12-07 14:41:11,230][19013] Num frames 1300... [2024-12-07 14:41:11,317][19013] Num frames 1400... [2024-12-07 14:41:11,404][19013] Num frames 1500... [2024-12-07 14:41:11,486][19013] Num frames 1600... [2024-12-07 14:41:11,568][19013] Avg episode rewards: #0: 18.160, true rewards: #0: 8.160 [2024-12-07 14:41:11,568][19013] Avg episode reward: 18.160, avg true_objective: 8.160 [2024-12-07 14:41:11,664][19013] Num frames 1700... [2024-12-07 14:41:11,758][19013] Num frames 1800... [2024-12-07 14:41:11,839][19013] Num frames 1900... [2024-12-07 14:41:11,919][19013] Num frames 2000... [2024-12-07 14:41:11,999][19013] Num frames 2100... [2024-12-07 14:41:12,114][19013] Avg episode rewards: #0: 15.587, true rewards: #0: 7.253 [2024-12-07 14:41:12,115][19013] Avg episode reward: 15.587, avg true_objective: 7.253 [2024-12-07 14:41:12,145][19013] Num frames 2200... [2024-12-07 14:41:12,265][19013] Num frames 2300... [2024-12-07 14:41:12,352][19013] Num frames 2400... [2024-12-07 14:41:12,432][19013] Num frames 2500... [2024-12-07 14:41:12,515][19013] Num frames 2600... [2024-12-07 14:41:12,596][19013] Num frames 2700... [2024-12-07 14:41:12,679][19013] Num frames 2800... [2024-12-07 14:41:12,762][19013] Num frames 2900... [2024-12-07 14:41:12,842][19013] Num frames 3000... [2024-12-07 14:41:12,922][19013] Num frames 3100... [2024-12-07 14:41:13,032][19013] Avg episode rewards: #0: 16.420, true rewards: #0: 7.920 [2024-12-07 14:41:13,033][19013] Avg episode reward: 16.420, avg true_objective: 7.920 [2024-12-07 14:41:13,072][19013] Num frames 3200... [2024-12-07 14:41:13,186][19013] Num frames 3300... [2024-12-07 14:41:13,297][19013] Num frames 3400... [2024-12-07 14:41:13,369][19013] Num frames 3500... [2024-12-07 14:41:13,439][19013] Num frames 3600... [2024-12-07 14:41:13,513][19013] Num frames 3700... [2024-12-07 14:41:13,585][19013] Num frames 3800... [2024-12-07 14:41:13,662][19013] Num frames 3900... [2024-12-07 14:41:13,745][19013] Num frames 4000... [2024-12-07 14:41:13,828][19013] Num frames 4100... [2024-12-07 14:41:13,912][19013] Num frames 4200... [2024-12-07 14:41:13,994][19013] Num frames 4300... [2024-12-07 14:41:14,077][19013] Num frames 4400... [2024-12-07 14:41:14,162][19013] Num frames 4500... [2024-12-07 14:41:14,246][19013] Num frames 4600... [2024-12-07 14:41:14,328][19013] Num frames 4700... [2024-12-07 14:41:14,409][19013] Num frames 4800... [2024-12-07 14:41:14,492][19013] Num frames 4900... [2024-12-07 14:41:14,621][19013] Avg episode rewards: #0: 21.584, true rewards: #0: 9.984 [2024-12-07 14:41:14,621][19013] Avg episode reward: 21.584, avg true_objective: 9.984 [2024-12-07 14:41:14,632][19013] Num frames 5000... [2024-12-07 14:41:14,751][19013] Num frames 5100... [2024-12-07 14:41:14,832][19013] Num frames 5200... [2024-12-07 14:41:14,902][19013] Num frames 5300... [2024-12-07 14:41:14,972][19013] Num frames 5400... [2024-12-07 14:41:15,052][19013] Num frames 5500... [2024-12-07 14:41:15,129][19013] Num frames 5600... [2024-12-07 14:41:15,199][19013] Num frames 5700... [2024-12-07 14:41:15,322][19013] Avg episode rewards: #0: 20.487, true rewards: #0: 9.653 [2024-12-07 14:41:15,323][19013] Avg episode reward: 20.487, avg true_objective: 9.653 [2024-12-07 14:41:15,331][19013] Num frames 5800... [2024-12-07 14:41:15,446][19013] Num frames 5900... [2024-12-07 14:41:15,525][19013] Num frames 6000... [2024-12-07 14:41:15,605][19013] Num frames 6100... [2024-12-07 14:41:15,684][19013] Num frames 6200... [2024-12-07 14:41:15,764][19013] Num frames 6300... [2024-12-07 14:41:15,842][19013] Num frames 6400... [2024-12-07 14:41:15,921][19013] Num frames 6500... [2024-12-07 14:41:16,002][19013] Num frames 6600... [2024-12-07 14:41:16,074][19013] Num frames 6700... [2024-12-07 14:41:16,143][19013] Num frames 6800... [2024-12-07 14:41:16,213][19013] Num frames 6900... [2024-12-07 14:41:16,281][19013] Num frames 7000... [2024-12-07 14:41:16,339][19013] Avg episode rewards: #0: 22.154, true rewards: #0: 10.011 [2024-12-07 14:41:16,340][19013] Avg episode reward: 22.154, avg true_objective: 10.011 [2024-12-07 14:41:16,439][19013] Num frames 7100... [2024-12-07 14:41:16,539][19013] Num frames 7200... [2024-12-07 14:41:16,607][19013] Num frames 7300... [2024-12-07 14:41:16,677][19013] Num frames 7400... [2024-12-07 14:41:16,749][19013] Num frames 7500... [2024-12-07 14:41:16,828][19013] Num frames 7600... [2024-12-07 14:41:16,906][19013] Num frames 7700... [2024-12-07 14:41:16,989][19013] Num frames 7800... [2024-12-07 14:41:17,085][19013] Avg episode rewards: #0: 21.191, true rewards: #0: 9.816 [2024-12-07 14:41:17,086][19013] Avg episode reward: 21.191, avg true_objective: 9.816 [2024-12-07 14:41:17,144][19013] Num frames 7900... [2024-12-07 14:41:17,259][19013] Num frames 8000... [2024-12-07 14:41:17,352][19013] Num frames 8100... [2024-12-07 14:41:17,432][19013] Num frames 8200... [2024-12-07 14:41:17,513][19013] Num frames 8300... [2024-12-07 14:41:17,595][19013] Num frames 8400... [2024-12-07 14:41:17,676][19013] Num frames 8500... [2024-12-07 14:41:17,758][19013] Num frames 8600... [2024-12-07 14:41:17,838][19013] Num frames 8700... [2024-12-07 14:41:17,906][19013] Avg episode rewards: #0: 20.908, true rewards: #0: 9.686 [2024-12-07 14:41:17,907][19013] Avg episode reward: 20.908, avg true_objective: 9.686 [2024-12-07 14:41:18,010][19013] Num frames 8800... [2024-12-07 14:41:18,113][19013] Num frames 8900... [2024-12-07 14:41:18,183][19013] Num frames 9000... [2024-12-07 14:41:18,255][19013] Num frames 9100... [2024-12-07 14:41:18,325][19013] Num frames 9200... [2024-12-07 14:41:18,420][19013] Avg episode rewards: #0: 19.561, true rewards: #0: 9.261 [2024-12-07 14:41:18,421][19013] Avg episode reward: 19.561, avg true_objective: 9.261 [2024-12-07 14:41:34,293][19013] Replay video saved to /home/rahatchd/code/deep-rl-hugging-face/unit8/train_dir/default_experiment/replay.mp4!