sf_log.txt

[2023-02-25 16:58:22,542][09465] Saving configuration to /content/train_dir/default_experiment/config.json...
[2023-02-25 16:58:22,545][09465] Rollout worker 0 uses device cpu
[2023-02-25 16:58:22,547][09465] Rollout worker 1 uses device cpu
[2023-02-25 16:58:22,548][09465] Rollout worker 2 uses device cpu
[2023-02-25 16:58:22,549][09465] Rollout worker 3 uses device cpu
[2023-02-25 16:58:22,551][09465] Rollout worker 4 uses device cpu
[2023-02-25 16:58:22,552][09465] Rollout worker 5 uses device cpu
[2023-02-25 16:58:22,553][09465] Rollout worker 6 uses device cpu
[2023-02-25 16:58:22,555][09465] Rollout worker 7 uses device cpu
[2023-02-25 16:58:22,748][09465] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-02-25 16:58:22,750][09465] InferenceWorker_p0-w0: min num requests: 2
[2023-02-25 16:58:22,782][09465] Starting all processes...
[2023-02-25 16:58:22,783][09465] Starting process learner_proc0
[2023-02-25 16:58:22,840][09465] Starting all processes...
[2023-02-25 16:58:22,858][09465] Starting process inference_proc0-0
[2023-02-25 16:58:22,858][09465] Starting process rollout_proc0
[2023-02-25 16:58:22,862][09465] Starting process rollout_proc1
[2023-02-25 16:58:22,889][09465] Starting process rollout_proc2
[2023-02-25 16:58:22,891][09465] Starting process rollout_proc3
[2023-02-25 16:58:22,891][09465] Starting process rollout_proc4
[2023-02-25 16:58:22,891][09465] Starting process rollout_proc5
[2023-02-25 16:58:22,891][09465] Starting process rollout_proc6
[2023-02-25 16:58:22,891][09465] Starting process rollout_proc7
[2023-02-25 16:58:34,547][15489] Worker 1 uses CPU cores [1]
[2023-02-25 16:58:34,618][15469] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-02-25 16:58:34,618][15469] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
[2023-02-25 16:58:34,859][15488] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-02-25 16:58:34,860][15488] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
[2023-02-25 16:58:34,864][15487] Worker 0 uses CPU cores [0]
[2023-02-25 16:58:34,879][15491] Worker 3 uses CPU cores [1]
[2023-02-25 16:58:34,968][15494] Worker 6 uses CPU cores [0]
[2023-02-25 16:58:34,973][15493] Worker 5 uses CPU cores [1]
[2023-02-25 16:58:34,996][15495] Worker 7 uses CPU cores [1]
[2023-02-25 16:58:35,016][15490] Worker 2 uses CPU cores [0]
[2023-02-25 16:58:35,018][15492] Worker 4 uses CPU cores [0]
[2023-02-25 16:58:35,465][15469] Num visible devices: 1
[2023-02-25 16:58:35,466][15488] Num visible devices: 1
[2023-02-25 16:58:35,477][15469] Starting seed is not provided
[2023-02-25 16:58:35,477][15469] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-02-25 16:58:35,478][15469] Initializing actor-critic model on device cuda:0
[2023-02-25 16:58:35,478][15469] RunningMeanStd input shape: (3, 72, 128)
[2023-02-25 16:58:35,480][15469] RunningMeanStd input shape: (1,)
[2023-02-25 16:58:35,492][15469] ConvEncoder: input_channels=3
[2023-02-25 16:58:35,767][15469] Conv encoder output size: 512
[2023-02-25 16:58:35,768][15469] Policy head output size: 512
[2023-02-25 16:58:35,816][15469] Created Actor Critic model with architecture:
[2023-02-25 16:58:35,817][15469] ActorCriticSharedWeights(
  (obs_normalizer): ObservationNormalizer(
    (running_mean_std): RunningMeanStdDictInPlace(
      (running_mean_std): ModuleDict(
        (obs): RunningMeanStdInPlace()
      )
    )
  )
  (returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
  (encoder): VizdoomEncoder(
    (basic_encoder): ConvEncoder(
      (enc): RecursiveScriptModule(
        original_name=ConvEncoderImpl
        (conv_head): RecursiveScriptModule(
          original_name=Sequential
          (0): RecursiveScriptModule(original_name=Conv2d)
          (1): RecursiveScriptModule(original_name=ELU)
          (2): RecursiveScriptModule(original_name=Conv2d)
          (3): RecursiveScriptModule(original_name=ELU)
          (4): RecursiveScriptModule(original_name=Conv2d)
          (5): RecursiveScriptModule(original_name=ELU)
        )
        (mlp_layers): RecursiveScriptModule(
          original_name=Sequential
          (0): RecursiveScriptModule(original_name=Linear)
          (1): RecursiveScriptModule(original_name=ELU)
        )
      )
    )
  )
  (core): ModelCoreRNN(
    (core): GRU(512, 512)
  )
  (decoder): MlpDecoder(
    (mlp): Identity()
  )
  (critic_linear): Linear(in_features=512, out_features=1, bias=True)
  (action_parameterization): ActionParameterizationDefault(
    (distribution_linear): Linear(in_features=512, out_features=5, bias=True)
  )
)
[2023-02-25 16:58:42,740][09465] Heartbeat connected on Batcher_0
[2023-02-25 16:58:42,748][09465] Heartbeat connected on InferenceWorker_p0-w0
[2023-02-25 16:58:42,758][09465] Heartbeat connected on RolloutWorker_w0
[2023-02-25 16:58:42,762][09465] Heartbeat connected on RolloutWorker_w1
[2023-02-25 16:58:42,765][09465] Heartbeat connected on RolloutWorker_w2
[2023-02-25 16:58:42,769][09465] Heartbeat connected on RolloutWorker_w3
[2023-02-25 16:58:42,772][09465] Heartbeat connected on RolloutWorker_w4
[2023-02-25 16:58:42,780][09465] Heartbeat connected on RolloutWorker_w5
[2023-02-25 16:58:42,781][09465] Heartbeat connected on RolloutWorker_w6
[2023-02-25 16:58:42,783][09465] Heartbeat connected on RolloutWorker_w7
[2023-02-25 16:58:42,902][15469] Using optimizer <class 'torch.optim.adam.Adam'>
[2023-02-25 16:58:42,903][15469] No checkpoints found
[2023-02-25 16:58:42,903][15469] Did not load from checkpoint, starting from scratch!
[2023-02-25 16:58:42,903][15469] Initialized policy 0 weights for model version 0
[2023-02-25 16:58:42,907][15469] LearnerWorker_p0 finished initialization!
[2023-02-25 16:58:42,910][15469] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-02-25 16:58:42,910][09465] Heartbeat connected on LearnerWorker_p0
[2023-02-25 16:58:43,179][15488] RunningMeanStd input shape: (3, 72, 128)
[2023-02-25 16:58:43,181][15488] RunningMeanStd input shape: (1,)
[2023-02-25 16:58:43,200][15488] ConvEncoder: input_channels=3
[2023-02-25 16:58:43,359][15488] Conv encoder output size: 512
[2023-02-25 16:58:43,360][15488] Policy head output size: 512
[2023-02-25 16:58:43,467][09465] Fps is (10 sec: nan, 60 sec: nan, 300 sec: nan). Total num frames: 0. Throughput: 0: nan. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
[2023-02-25 16:58:46,580][09465] Inference worker 0-0 is ready!
[2023-02-25 16:58:46,583][09465] All inference workers are ready! Signal rollout workers to start!
[2023-02-25 16:58:46,728][15495] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-25 16:58:46,746][15489] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-25 16:58:46,751][15491] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-25 16:58:46,790][15493] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-25 16:58:46,820][15492] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-25 16:58:46,823][15490] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-25 16:58:46,827][15487] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-25 16:58:46,859][15494] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-25 16:58:48,184][15495] Decorrelating experience for 0 frames...
[2023-02-25 16:58:48,186][15493] Decorrelating experience for 0 frames...
[2023-02-25 16:58:48,187][15491] Decorrelating experience for 0 frames...
[2023-02-25 16:58:48,314][15487] Decorrelating experience for 0 frames...
[2023-02-25 16:58:48,319][15490] Decorrelating experience for 0 frames...
[2023-02-25 16:58:48,322][15492] Decorrelating experience for 0 frames...
[2023-02-25 16:58:48,467][09465] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 0.0. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
[2023-02-25 16:58:49,041][15492] Decorrelating experience for 32 frames...
[2023-02-25 16:58:49,061][15494] Decorrelating experience for 0 frames...
[2023-02-25 16:58:49,217][15493] Decorrelating experience for 32 frames...
[2023-02-25 16:58:49,220][15489] Decorrelating experience for 0 frames...
[2023-02-25 16:58:49,453][15491] Decorrelating experience for 32 frames...
[2023-02-25 16:58:50,058][15492] Decorrelating experience for 64 frames...
[2023-02-25 16:58:50,111][15489] Decorrelating experience for 32 frames...
[2023-02-25 16:58:50,239][15493] Decorrelating experience for 64 frames...
[2023-02-25 16:58:50,486][15490] Decorrelating experience for 32 frames...
[2023-02-25 16:58:50,492][15487] Decorrelating experience for 32 frames...
[2023-02-25 16:58:50,787][15494] Decorrelating experience for 32 frames...
[2023-02-25 16:58:51,592][15489] Decorrelating experience for 64 frames...
[2023-02-25 16:58:51,612][15491] Decorrelating experience for 64 frames...
[2023-02-25 16:58:51,639][15492] Decorrelating experience for 96 frames...
[2023-02-25 16:58:51,764][15493] Decorrelating experience for 96 frames...
[2023-02-25 16:58:51,843][15487] Decorrelating experience for 64 frames...
[2023-02-25 16:58:51,983][15495] Decorrelating experience for 32 frames...
[2023-02-25 16:58:52,099][15494] Decorrelating experience for 64 frames...
[2023-02-25 16:58:52,985][15489] Decorrelating experience for 96 frames...
[2023-02-25 16:58:53,130][15487] Decorrelating experience for 96 frames...
[2023-02-25 16:58:53,212][15491] Decorrelating experience for 96 frames...
[2023-02-25 16:58:53,365][15495] Decorrelating experience for 64 frames...
[2023-02-25 16:58:53,451][15494] Decorrelating experience for 96 frames...
[2023-02-25 16:58:53,468][09465] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 0.0. Samples: 0. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
[2023-02-25 16:58:53,847][15495] Decorrelating experience for 96 frames...
[2023-02-25 16:58:54,057][15490] Decorrelating experience for 64 frames...
[2023-02-25 16:58:54,385][15490] Decorrelating experience for 96 frames...
[2023-02-25 16:58:58,467][09465] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 2.9. Samples: 44. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
[2023-02-25 16:58:58,472][09465] Avg episode reward: [(0, '1.685')]
[2023-02-25 16:58:58,903][15469] Signal inference workers to stop experience collection...
[2023-02-25 16:58:58,934][15488] InferenceWorker_p0-w0: stopping experience collection
[2023-02-25 16:59:01,813][15469] Signal inference workers to resume experience collection...
[2023-02-25 16:59:01,814][15488] InferenceWorker_p0-w0: resuming experience collection
[2023-02-25 16:59:03,469][09465] Fps is (10 sec: 409.5, 60 sec: 204.8, 300 sec: 204.8). Total num frames: 4096. Throughput: 0: 115.3. Samples: 2306. Policy #0 lag: (min: 0.0, avg: 0.0, max: 0.0)
[2023-02-25 16:59:03,476][09465] Avg episode reward: [(0, '2.362')]
[2023-02-25 16:59:08,467][09465] Fps is (10 sec: 2867.2, 60 sec: 1146.9, 300 sec: 1146.9). Total num frames: 28672. Throughput: 0: 258.5. Samples: 6462. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0)
[2023-02-25 16:59:08,475][09465] Avg episode reward: [(0, '3.760')]
[2023-02-25 16:59:11,424][15488] Updated weights for policy 0, policy_version 10 (0.0014)
[2023-02-25 16:59:13,467][09465] Fps is (10 sec: 4506.3, 60 sec: 1638.4, 300 sec: 1638.4). Total num frames: 49152. Throughput: 0: 328.0. Samples: 9840. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 16:59:13,470][09465] Avg episode reward: [(0, '4.381')]
[2023-02-25 16:59:18,468][09465] Fps is (10 sec: 3686.4, 60 sec: 1872.5, 300 sec: 1872.5). Total num frames: 65536. Throughput: 0: 463.9. Samples: 16238. Policy #0 lag: (min: 0.0, avg: 0.3, max: 2.0)
[2023-02-25 16:59:18,472][09465] Avg episode reward: [(0, '4.376')]
[2023-02-25 16:59:23,056][15488] Updated weights for policy 0, policy_version 20 (0.0014)
[2023-02-25 16:59:23,467][09465] Fps is (10 sec: 3276.8, 60 sec: 2048.0, 300 sec: 2048.0). Total num frames: 81920. Throughput: 0: 517.0. Samples: 20678. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-25 16:59:23,470][09465] Avg episode reward: [(0, '4.431')]
[2023-02-25 16:59:28,467][09465] Fps is (10 sec: 3686.4, 60 sec: 2275.6, 300 sec: 2275.6). Total num frames: 102400. Throughput: 0: 510.0. Samples: 22950. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 16:59:28,469][09465] Avg episode reward: [(0, '4.296')]
[2023-02-25 16:59:28,482][15469] Saving new best policy, reward=4.296!
[2023-02-25 16:59:32,757][15488] Updated weights for policy 0, policy_version 30 (0.0023)
[2023-02-25 16:59:33,474][09465] Fps is (10 sec: 4093.1, 60 sec: 2457.3, 300 sec: 2457.3). Total num frames: 122880. Throughput: 0: 663.4. Samples: 29858. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 16:59:33,478][09465] Avg episode reward: [(0, '4.362')]
[2023-02-25 16:59:33,482][15469] Saving new best policy, reward=4.362!
[2023-02-25 16:59:38,467][09465] Fps is (10 sec: 4096.0, 60 sec: 2606.5, 300 sec: 2606.5). Total num frames: 143360. Throughput: 0: 794.6. Samples: 35758. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 16:59:38,481][09465] Avg episode reward: [(0, '4.358')]
[2023-02-25 16:59:43,468][09465] Fps is (10 sec: 3279.1, 60 sec: 2594.1, 300 sec: 2594.1). Total num frames: 155648. Throughput: 0: 843.1. Samples: 37982. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 16:59:43,470][09465] Avg episode reward: [(0, '4.207')]
[2023-02-25 16:59:45,543][15488] Updated weights for policy 0, policy_version 40 (0.0025)
[2023-02-25 16:59:48,467][09465] Fps is (10 sec: 3276.8, 60 sec: 2935.5, 300 sec: 2709.7). Total num frames: 176128. Throughput: 0: 898.8. Samples: 42750. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 16:59:48,469][09465] Avg episode reward: [(0, '4.241')]
[2023-02-25 16:59:53,468][09465] Fps is (10 sec: 3686.4, 60 sec: 3208.5, 300 sec: 2750.2). Total num frames: 192512. Throughput: 0: 941.8. Samples: 48842. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 16:59:53,470][09465] Avg episode reward: [(0, '4.382')]
[2023-02-25 16:59:53,476][15469] Saving new best policy, reward=4.382!
[2023-02-25 16:59:56,466][15488] Updated weights for policy 0, policy_version 50 (0.0014)
[2023-02-25 16:59:58,469][09465] Fps is (10 sec: 3276.3, 60 sec: 3481.5, 300 sec: 2785.2). Total num frames: 208896. Throughput: 0: 915.1. Samples: 51020. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 16:59:58,472][09465] Avg episode reward: [(0, '4.373')]
[2023-02-25 17:00:03,467][09465] Fps is (10 sec: 2867.2, 60 sec: 3618.2, 300 sec: 2764.8). Total num frames: 221184. Throughput: 0: 870.7. Samples: 55418. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:00:03,470][09465] Avg episode reward: [(0, '4.384')]
[2023-02-25 17:00:03,480][15469] Saving new best policy, reward=4.384!
[2023-02-25 17:00:08,467][09465] Fps is (10 sec: 3277.3, 60 sec: 3549.9, 300 sec: 2843.1). Total num frames: 241664. Throughput: 0: 883.4. Samples: 60432. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-25 17:00:08,471][09465] Avg episode reward: [(0, '4.520')]
[2023-02-25 17:00:08,484][15469] Saving new best policy, reward=4.520!
[2023-02-25 17:00:08,939][15488] Updated weights for policy 0, policy_version 60 (0.0033)
[2023-02-25 17:00:13,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 2912.7). Total num frames: 262144. Throughput: 0: 907.0. Samples: 63764. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:00:13,473][09465] Avg episode reward: [(0, '4.478')]
[2023-02-25 17:00:18,468][09465] Fps is (10 sec: 4095.8, 60 sec: 3618.1, 300 sec: 2975.0). Total num frames: 282624. Throughput: 0: 897.0. Samples: 70218. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:00:18,474][09465] Avg episode reward: [(0, '4.411')]
[2023-02-25 17:00:18,489][15469] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000069_282624.pth...
[2023-02-25 17:00:19,513][15488] Updated weights for policy 0, policy_version 70 (0.0019)
[2023-02-25 17:00:23,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 2949.1). Total num frames: 294912. Throughput: 0: 860.4. Samples: 74474. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:00:23,473][09465] Avg episode reward: [(0, '4.402')]
[2023-02-25 17:00:28,468][09465] Fps is (10 sec: 3276.9, 60 sec: 3549.9, 300 sec: 3003.7). Total num frames: 315392. Throughput: 0: 860.9. Samples: 76724. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:00:28,474][09465] Avg episode reward: [(0, '4.349')]
[2023-02-25 17:00:30,524][15488] Updated weights for policy 0, policy_version 80 (0.0018)
[2023-02-25 17:00:33,468][09465] Fps is (10 sec: 4505.5, 60 sec: 3618.5, 300 sec: 3090.6). Total num frames: 339968. Throughput: 0: 909.6. Samples: 83682. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:00:33,470][09465] Avg episode reward: [(0, '4.407')]
[2023-02-25 17:00:38,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3098.7). Total num frames: 356352. Throughput: 0: 908.5. Samples: 89726. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:00:38,473][09465] Avg episode reward: [(0, '4.313')]
[2023-02-25 17:00:41,808][15488] Updated weights for policy 0, policy_version 90 (0.0018)
[2023-02-25 17:00:43,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3618.1, 300 sec: 3106.1). Total num frames: 372736. Throughput: 0: 908.4. Samples: 91896. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:00:43,472][09465] Avg episode reward: [(0, '4.322')]
[2023-02-25 17:00:48,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3113.0). Total num frames: 389120. Throughput: 0: 918.3. Samples: 96742. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-25 17:00:48,474][09465] Avg episode reward: [(0, '4.391')]
[2023-02-25 17:00:52,086][15488] Updated weights for policy 0, policy_version 100 (0.0021)
[2023-02-25 17:00:53,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3182.3). Total num frames: 413696. Throughput: 0: 957.3. Samples: 103510. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:00:53,470][09465] Avg episode reward: [(0, '4.341')]
[2023-02-25 17:00:58,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3686.5, 300 sec: 3185.8). Total num frames: 430080. Throughput: 0: 957.2. Samples: 106840. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:00:58,482][09465] Avg episode reward: [(0, '4.413')]
[2023-02-25 17:01:03,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3189.0). Total num frames: 446464. Throughput: 0: 910.5. Samples: 111188. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:01:03,474][09465] Avg episode reward: [(0, '4.468')]
[2023-02-25 17:01:04,354][15488] Updated weights for policy 0, policy_version 110 (0.0020)
[2023-02-25 17:01:08,468][09465] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3220.3). Total num frames: 466944. Throughput: 0: 930.1. Samples: 116328. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:01:08,470][09465] Avg episode reward: [(0, '4.405')]
[2023-02-25 17:01:13,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3249.5). Total num frames: 487424. Throughput: 0: 957.0. Samples: 119788. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:01:13,472][09465] Avg episode reward: [(0, '4.431')]
[2023-02-25 17:01:13,884][15488] Updated weights for policy 0, policy_version 120 (0.0018)
[2023-02-25 17:01:18,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3686.4, 300 sec: 3250.4). Total num frames: 503808. Throughput: 0: 947.5. Samples: 126320. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
[2023-02-25 17:01:18,477][09465] Avg episode reward: [(0, '4.511')]
[2023-02-25 17:01:23,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3251.2). Total num frames: 520192. Throughput: 0: 910.4. Samples: 130692. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:01:23,470][09465] Avg episode reward: [(0, '4.546')]
[2023-02-25 17:01:23,476][15469] Saving new best policy, reward=4.546!
[2023-02-25 17:01:26,464][15488] Updated weights for policy 0, policy_version 130 (0.0020)
[2023-02-25 17:01:28,491][09465] Fps is (10 sec: 3677.9, 60 sec: 3753.2, 300 sec: 3276.3). Total num frames: 540672. Throughput: 0: 908.9. Samples: 132816. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:01:28,499][09465] Avg episode reward: [(0, '4.520')]
[2023-02-25 17:01:33,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3300.9). Total num frames: 561152. Throughput: 0: 954.6. Samples: 139698. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:01:33,475][09465] Avg episode reward: [(0, '4.543')]
[2023-02-25 17:01:35,495][15488] Updated weights for policy 0, policy_version 140 (0.0032)
[2023-02-25 17:01:38,467][09465] Fps is (10 sec: 4105.5, 60 sec: 3754.7, 300 sec: 3323.6). Total num frames: 581632. Throughput: 0: 939.9. Samples: 145804. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:01:38,474][09465] Avg episode reward: [(0, '4.687')]
[2023-02-25 17:01:38,487][15469] Saving new best policy, reward=4.687!
[2023-02-25 17:01:43,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3322.3). Total num frames: 598016. Throughput: 0: 913.8. Samples: 147960. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:01:43,474][09465] Avg episode reward: [(0, '4.514')]
[2023-02-25 17:01:48,016][15488] Updated weights for policy 0, policy_version 150 (0.0015)
[2023-02-25 17:01:48,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3321.1). Total num frames: 614400. Throughput: 0: 918.6. Samples: 152524. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:01:48,470][09465] Avg episode reward: [(0, '4.204')]
[2023-02-25 17:01:53,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3686.4, 300 sec: 3341.5). Total num frames: 634880. Throughput: 0: 957.5. Samples: 159416. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:01:53,475][09465] Avg episode reward: [(0, '4.462')]
[2023-02-25 17:01:57,795][15488] Updated weights for policy 0, policy_version 160 (0.0017)
[2023-02-25 17:01:58,468][09465] Fps is (10 sec: 4095.9, 60 sec: 3754.6, 300 sec: 3360.8). Total num frames: 655360. Throughput: 0: 956.7. Samples: 162838. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:01:58,470][09465] Avg episode reward: [(0, '4.690')]
[2023-02-25 17:01:58,485][15469] Saving new best policy, reward=4.690!
[2023-02-25 17:02:03,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3686.4, 300 sec: 3338.2). Total num frames: 667648. Throughput: 0: 893.6. Samples: 166534. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:02:03,470][09465] Avg episode reward: [(0, '4.736')]
[2023-02-25 17:02:03,474][15469] Saving new best policy, reward=4.736!
[2023-02-25 17:02:08,469][09465] Fps is (10 sec: 2457.3, 60 sec: 3549.8, 300 sec: 3316.7). Total num frames: 679936. Throughput: 0: 873.8. Samples: 170014. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:02:08,472][09465] Avg episode reward: [(0, '4.589')]
[2023-02-25 17:02:13,297][15488] Updated weights for policy 0, policy_version 170 (0.0043)
[2023-02-25 17:02:13,467][09465] Fps is (10 sec: 2867.2, 60 sec: 3481.6, 300 sec: 3315.8). Total num frames: 696320. Throughput: 0: 870.3. Samples: 171960. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:02:13,474][09465] Avg episode reward: [(0, '4.563')]
[2023-02-25 17:02:18,467][09465] Fps is (10 sec: 3687.0, 60 sec: 3549.9, 300 sec: 3334.0). Total num frames: 716800. Throughput: 0: 863.1. Samples: 178538. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:02:18,469][09465] Avg episode reward: [(0, '4.455')]
[2023-02-25 17:02:18,486][15469] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000175_716800.pth...
[2023-02-25 17:02:22,582][15488] Updated weights for policy 0, policy_version 180 (0.0022)
[2023-02-25 17:02:23,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3618.1, 300 sec: 3351.3). Total num frames: 737280. Throughput: 0: 869.0. Samples: 184908. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:02:23,473][09465] Avg episode reward: [(0, '4.441')]
[2023-02-25 17:02:28,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3551.2, 300 sec: 3349.6). Total num frames: 753664. Throughput: 0: 869.5. Samples: 187088. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:02:28,473][09465] Avg episode reward: [(0, '4.441')]
[2023-02-25 17:02:33,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3348.0). Total num frames: 770048. Throughput: 0: 866.8. Samples: 191528. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
[2023-02-25 17:02:33,472][09465] Avg episode reward: [(0, '4.606')]
[2023-02-25 17:02:34,754][15488] Updated weights for policy 0, policy_version 190 (0.0023)
[2023-02-25 17:02:38,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3381.4). Total num frames: 794624. Throughput: 0: 864.3. Samples: 198308. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0)
[2023-02-25 17:02:38,472][09465] Avg episode reward: [(0, '4.747')]
[2023-02-25 17:02:38,486][15469] Saving new best policy, reward=4.747!
[2023-02-25 17:02:43,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3379.2). Total num frames: 811008. Throughput: 0: 863.5. Samples: 201694. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:02:43,469][09465] Avg episode reward: [(0, '4.766')]
[2023-02-25 17:02:43,476][15469] Saving new best policy, reward=4.766!
[2023-02-25 17:02:45,064][15488] Updated weights for policy 0, policy_version 200 (0.0029)
[2023-02-25 17:02:48,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3549.9, 300 sec: 3377.1). Total num frames: 827392. Throughput: 0: 885.6. Samples: 206384. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:02:48,473][09465] Avg episode reward: [(0, '4.678')]
[2023-02-25 17:02:53,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3375.1). Total num frames: 843776. Throughput: 0: 914.7. Samples: 211172. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-25 17:02:53,470][09465] Avg episode reward: [(0, '4.643')]
[2023-02-25 17:02:56,478][15488] Updated weights for policy 0, policy_version 210 (0.0025)
[2023-02-25 17:02:58,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3405.3). Total num frames: 868352. Throughput: 0: 947.9. Samples: 214616. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-25 17:02:58,472][09465] Avg episode reward: [(0, '4.467')]
[2023-02-25 17:03:03,470][09465] Fps is (10 sec: 4504.4, 60 sec: 3686.2, 300 sec: 3418.6). Total num frames: 888832. Throughput: 0: 956.1. Samples: 221566. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:03:03,473][09465] Avg episode reward: [(0, '4.749')]
[2023-02-25 17:03:07,389][15488] Updated weights for policy 0, policy_version 220 (0.0032)
[2023-02-25 17:03:08,472][09465] Fps is (10 sec: 3275.3, 60 sec: 3686.2, 300 sec: 3400.4). Total num frames: 901120. Throughput: 0: 913.5. Samples: 226020. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:03:08,475][09465] Avg episode reward: [(0, '4.785')]
[2023-02-25 17:03:08,492][15469] Saving new best policy, reward=4.785!
[2023-02-25 17:03:13,467][09465] Fps is (10 sec: 2867.9, 60 sec: 3686.4, 300 sec: 3398.2). Total num frames: 917504. Throughput: 0: 913.3. Samples: 228186. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:03:13,470][09465] Avg episode reward: [(0, '4.545')]
[2023-02-25 17:03:17,961][15488] Updated weights for policy 0, policy_version 230 (0.0022)
[2023-02-25 17:03:18,467][09465] Fps is (10 sec: 4097.9, 60 sec: 3754.7, 300 sec: 3425.7). Total num frames: 942080. Throughput: 0: 955.3. Samples: 234516. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:03:18,470][09465] Avg episode reward: [(0, '4.439')]
[2023-02-25 17:03:23,467][09465] Fps is (10 sec: 4505.6, 60 sec: 3754.7, 300 sec: 3437.7). Total num frames: 962560. Throughput: 0: 953.9. Samples: 241232. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:03:23,470][09465] Avg episode reward: [(0, '4.627')]
[2023-02-25 17:03:28,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3434.9). Total num frames: 978944. Throughput: 0: 926.0. Samples: 243366. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:03:28,472][09465] Avg episode reward: [(0, '4.775')]
[2023-02-25 17:03:29,597][15488] Updated weights for policy 0, policy_version 240 (0.0011)
[2023-02-25 17:03:33,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3432.2). Total num frames: 995328. Throughput: 0: 918.9. Samples: 247736. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:03:33,471][09465] Avg episode reward: [(0, '4.699')]
[2023-02-25 17:03:38,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3686.4, 300 sec: 3443.4). Total num frames: 1015808. Throughput: 0: 958.6. Samples: 254310. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:03:38,477][09465] Avg episode reward: [(0, '4.731')]
[2023-02-25 17:03:39,546][15488] Updated weights for policy 0, policy_version 250 (0.0012)
[2023-02-25 17:03:43,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3512.8). Total num frames: 1036288. Throughput: 0: 956.8. Samples: 257672. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:03:43,474][09465] Avg episode reward: [(0, '4.796')]
[2023-02-25 17:03:43,552][15469] Saving new best policy, reward=4.796!
[2023-02-25 17:03:48,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3568.4). Total num frames: 1052672. Throughput: 0: 915.6. Samples: 262764. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:03:48,470][09465] Avg episode reward: [(0, '4.815')]
[2023-02-25 17:03:48,490][15469] Saving new best policy, reward=4.815!
[2023-02-25 17:03:52,326][15488] Updated weights for policy 0, policy_version 260 (0.0031)
[2023-02-25 17:03:53,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3623.9). Total num frames: 1069056. Throughput: 0: 914.1. Samples: 267148. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:03:53,469][09465] Avg episode reward: [(0, '4.858')]
[2023-02-25 17:03:53,480][15469] Saving new best policy, reward=4.858!
[2023-02-25 17:03:58,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3686.4, 300 sec: 3679.5). Total num frames: 1089536. Throughput: 0: 938.5. Samples: 270418. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:03:58,470][09465] Avg episode reward: [(0, '5.118')]
[2023-02-25 17:03:58,484][15469] Saving new best policy, reward=5.118!
[2023-02-25 17:04:01,436][15488] Updated weights for policy 0, policy_version 270 (0.0020)
[2023-02-25 17:04:03,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3686.6, 300 sec: 3665.6). Total num frames: 1110016. Throughput: 0: 948.0. Samples: 277178. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:04:03,470][09465] Avg episode reward: [(0, '5.170')]
[2023-02-25 17:04:03,524][15469] Saving new best policy, reward=5.170!
[2023-02-25 17:04:08,470][09465] Fps is (10 sec: 3685.4, 60 sec: 3754.8, 300 sec: 3651.7). Total num frames: 1126400. Throughput: 0: 904.8. Samples: 281950. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:04:08,473][09465] Avg episode reward: [(0, '5.106')]
[2023-02-25 17:04:13,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3651.7). Total num frames: 1142784. Throughput: 0: 905.4. Samples: 284108. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:04:13,474][09465] Avg episode reward: [(0, '4.946')]
[2023-02-25 17:04:14,214][15488] Updated weights for policy 0, policy_version 280 (0.0027)
[2023-02-25 17:04:18,468][09465] Fps is (10 sec: 3687.3, 60 sec: 3686.4, 300 sec: 3665.6). Total num frames: 1163264. Throughput: 0: 940.9. Samples: 290078. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:04:18,475][09465] Avg episode reward: [(0, '5.003')]
[2023-02-25 17:04:18,486][15469] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000284_1163264.pth...
[2023-02-25 17:04:18,606][15469] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000069_282624.pth
[2023-02-25 17:04:23,163][15488] Updated weights for policy 0, policy_version 290 (0.0018)
[2023-02-25 17:04:23,467][09465] Fps is (10 sec: 4505.6, 60 sec: 3754.7, 300 sec: 3679.5). Total num frames: 1187840. Throughput: 0: 944.9. Samples: 296832. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-25 17:04:23,472][09465] Avg episode reward: [(0, '4.661')]
[2023-02-25 17:04:28,468][09465] Fps is (10 sec: 3686.3, 60 sec: 3686.4, 300 sec: 3651.8). Total num frames: 1200128. Throughput: 0: 923.0. Samples: 299206. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:04:28,477][09465] Avg episode reward: [(0, '4.540')]
[2023-02-25 17:04:33,468][09465] Fps is (10 sec: 2867.1, 60 sec: 3686.4, 300 sec: 3637.8). Total num frames: 1216512. Throughput: 0: 906.8. Samples: 303572. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:04:33,475][09465] Avg episode reward: [(0, '4.780')]
[2023-02-25 17:04:35,905][15488] Updated weights for policy 0, policy_version 300 (0.0033)
[2023-02-25 17:04:38,468][09465] Fps is (10 sec: 3686.5, 60 sec: 3686.4, 300 sec: 3665.6). Total num frames: 1236992. Throughput: 0: 947.2. Samples: 309772. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:04:38,470][09465] Avg episode reward: [(0, '4.928')]
[2023-02-25 17:04:43,473][09465] Fps is (10 sec: 4503.0, 60 sec: 3754.3, 300 sec: 3679.4). Total num frames: 1261568. Throughput: 0: 950.1. Samples: 313178. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:04:43,477][09465] Avg episode reward: [(0, '5.008')]
[2023-02-25 17:04:45,785][15488] Updated weights for policy 0, policy_version 310 (0.0019)
[2023-02-25 17:04:48,471][09465] Fps is (10 sec: 3685.1, 60 sec: 3686.2, 300 sec: 3665.5). Total num frames: 1273856. Throughput: 0: 921.2. Samples: 318636. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-25 17:04:48,477][09465] Avg episode reward: [(0, '5.022')]
[2023-02-25 17:04:53,468][09465] Fps is (10 sec: 2868.8, 60 sec: 3686.4, 300 sec: 3665.6). Total num frames: 1290240. Throughput: 0: 914.8. Samples: 323112. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:04:53,474][09465] Avg episode reward: [(0, '4.968')]
[2023-02-25 17:04:57,568][15488] Updated weights for policy 0, policy_version 320 (0.0026)
[2023-02-25 17:04:58,467][09465] Fps is (10 sec: 4097.5, 60 sec: 3754.7, 300 sec: 3707.2). Total num frames: 1314816. Throughput: 0: 933.6. Samples: 326120. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:04:58,470][09465] Avg episode reward: [(0, '4.706')]
[2023-02-25 17:05:03,467][09465] Fps is (10 sec: 4505.8, 60 sec: 3754.7, 300 sec: 3707.2). Total num frames: 1335296. Throughput: 0: 953.1. Samples: 332968. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:05:03,469][09465] Avg episode reward: [(0, '5.011')]
[2023-02-25 17:05:08,211][15488] Updated weights for policy 0, policy_version 330 (0.0027)
[2023-02-25 17:05:08,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3754.8, 300 sec: 3693.3). Total num frames: 1351680. Throughput: 0: 917.4. Samples: 338114. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:05:08,473][09465] Avg episode reward: [(0, '5.247')]
[2023-02-25 17:05:08,485][15469] Saving new best policy, reward=5.247!
[2023-02-25 17:05:13,467][09465] Fps is (10 sec: 2867.2, 60 sec: 3686.4, 300 sec: 3665.6). Total num frames: 1363968. Throughput: 0: 911.3. Samples: 340214. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:05:13,471][09465] Avg episode reward: [(0, '5.147')]
[2023-02-25 17:05:18,468][09465] Fps is (10 sec: 3686.3, 60 sec: 3754.7, 300 sec: 3707.2). Total num frames: 1388544. Throughput: 0: 935.6. Samples: 345672. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:05:18,474][09465] Avg episode reward: [(0, '5.321')]
[2023-02-25 17:05:18,483][15469] Saving new best policy, reward=5.321!
[2023-02-25 17:05:19,277][15488] Updated weights for policy 0, policy_version 340 (0.0021)
[2023-02-25 17:05:23,469][09465] Fps is (10 sec: 4504.7, 60 sec: 3686.3, 300 sec: 3707.2). Total num frames: 1409024. Throughput: 0: 951.3. Samples: 352582. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-25 17:05:23,482][09465] Avg episode reward: [(0, '5.279')]
[2023-02-25 17:05:28,467][09465] Fps is (10 sec: 3686.5, 60 sec: 3754.7, 300 sec: 3679.5). Total num frames: 1425408. Throughput: 0: 935.5. Samples: 355272. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:05:28,473][09465] Avg episode reward: [(0, '5.116')]
[2023-02-25 17:05:30,656][15488] Updated weights for policy 0, policy_version 350 (0.0030)
[2023-02-25 17:05:33,469][09465] Fps is (10 sec: 2867.4, 60 sec: 3686.3, 300 sec: 3665.6). Total num frames: 1437696. Throughput: 0: 911.3. Samples: 359644. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:05:33,476][09465] Avg episode reward: [(0, '4.959')]
[2023-02-25 17:05:38,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3693.3). Total num frames: 1462272. Throughput: 0: 942.1. Samples: 365504. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:05:38,475][09465] Avg episode reward: [(0, '5.159')]
[2023-02-25 17:05:40,830][15488] Updated weights for policy 0, policy_version 360 (0.0019)
[2023-02-25 17:05:43,467][09465] Fps is (10 sec: 4506.2, 60 sec: 3686.8, 300 sec: 3707.2). Total num frames: 1482752. Throughput: 0: 952.4. Samples: 368976. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:05:43,475][09465] Avg episode reward: [(0, '5.250')]
[2023-02-25 17:05:48,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3754.9, 300 sec: 3679.5). Total num frames: 1499136. Throughput: 0: 927.6. Samples: 374710. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:05:48,475][09465] Avg episode reward: [(0, '5.598')]
[2023-02-25 17:05:48,488][15469] Saving new best policy, reward=5.598!
[2023-02-25 17:05:53,073][15488] Updated weights for policy 0, policy_version 370 (0.0025)
[2023-02-25 17:05:53,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3679.5). Total num frames: 1515520. Throughput: 0: 911.1. Samples: 379112. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:05:53,474][09465] Avg episode reward: [(0, '5.390')]
[2023-02-25 17:05:58,472][09465] Fps is (10 sec: 3684.6, 60 sec: 3686.1, 300 sec: 3693.3). Total num frames: 1536000. Throughput: 0: 925.5. Samples: 381866. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:05:58,476][09465] Avg episode reward: [(0, '5.857')]
[2023-02-25 17:05:58,490][15469] Saving new best policy, reward=5.857!
[2023-02-25 17:06:02,435][15488] Updated weights for policy 0, policy_version 380 (0.0020)
[2023-02-25 17:06:03,467][09465] Fps is (10 sec: 4505.6, 60 sec: 3754.7, 300 sec: 3707.2). Total num frames: 1560576. Throughput: 0: 959.8. Samples: 388862. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:06:03,470][09465] Avg episode reward: [(0, '6.012')]
[2023-02-25 17:06:03,477][15469] Saving new best policy, reward=6.012!
[2023-02-25 17:06:08,473][09465] Fps is (10 sec: 4095.9, 60 sec: 3754.3, 300 sec: 3693.3). Total num frames: 1576960. Throughput: 0: 925.8. Samples: 394244. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-25 17:06:08,480][09465] Avg episode reward: [(0, '5.846')]
[2023-02-25 17:06:13,467][09465] Fps is (10 sec: 2867.2, 60 sec: 3754.7, 300 sec: 3679.5). Total num frames: 1589248. Throughput: 0: 914.9. Samples: 396444. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:06:13,472][09465] Avg episode reward: [(0, '5.935')]
[2023-02-25 17:06:14,973][15488] Updated weights for policy 0, policy_version 390 (0.0045)
[2023-02-25 17:06:18,468][09465] Fps is (10 sec: 3278.5, 60 sec: 3686.4, 300 sec: 3693.3). Total num frames: 1609728. Throughput: 0: 934.2. Samples: 401682. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:06:18,470][09465] Avg episode reward: [(0, '6.130')]
[2023-02-25 17:06:18,480][15469] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000393_1609728.pth...
[2023-02-25 17:06:18,606][15469] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000175_716800.pth
[2023-02-25 17:06:18,618][15469] Saving new best policy, reward=6.130!
[2023-02-25 17:06:23,467][09465] Fps is (10 sec: 4505.6, 60 sec: 3754.8, 300 sec: 3707.5). Total num frames: 1634304. Throughput: 0: 950.8. Samples: 408288. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:06:23,469][09465] Avg episode reward: [(0, '6.004')]
[2023-02-25 17:06:24,276][15488] Updated weights for policy 0, policy_version 400 (0.0017)
[2023-02-25 17:06:28,472][09465] Fps is (10 sec: 3684.7, 60 sec: 3686.1, 300 sec: 3679.4). Total num frames: 1646592. Throughput: 0: 939.6. Samples: 411262. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:06:28,474][09465] Avg episode reward: [(0, '6.162')]
[2023-02-25 17:06:28,504][15469] Saving new best policy, reward=6.162!
[2023-02-25 17:06:33,467][09465] Fps is (10 sec: 2867.2, 60 sec: 3754.7, 300 sec: 3665.6). Total num frames: 1662976. Throughput: 0: 906.3. Samples: 415492. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:06:33,473][09465] Avg episode reward: [(0, '5.987')]
[2023-02-25 17:06:37,177][15488] Updated weights for policy 0, policy_version 410 (0.0022)
[2023-02-25 17:06:38,467][09465] Fps is (10 sec: 3688.1, 60 sec: 3686.4, 300 sec: 3679.5). Total num frames: 1683456. Throughput: 0: 930.9. Samples: 421004. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:06:38,470][09465] Avg episode reward: [(0, '5.859')]
[2023-02-25 17:06:43,468][09465] Fps is (10 sec: 4505.5, 60 sec: 3754.7, 300 sec: 3707.2). Total num frames: 1708032. Throughput: 0: 947.7. Samples: 424510. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:06:43,471][09465] Avg episode reward: [(0, '6.327')]
[2023-02-25 17:06:43,472][15469] Saving new best policy, reward=6.327!
[2023-02-25 17:06:46,987][15488] Updated weights for policy 0, policy_version 420 (0.0014)
[2023-02-25 17:06:48,469][09465] Fps is (10 sec: 4095.4, 60 sec: 3754.6, 300 sec: 3693.3). Total num frames: 1724416. Throughput: 0: 926.5. Samples: 430554. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:06:48,472][09465] Avg episode reward: [(0, '6.736')]
[2023-02-25 17:06:48,487][15469] Saving new best policy, reward=6.736!
[2023-02-25 17:06:53,467][09465] Fps is (10 sec: 2867.3, 60 sec: 3686.4, 300 sec: 3665.6). Total num frames: 1736704. Throughput: 0: 901.4. Samples: 434802. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:06:53,476][09465] Avg episode reward: [(0, '7.251')]
[2023-02-25 17:06:53,485][15469] Saving new best policy, reward=7.251!
[2023-02-25 17:06:58,467][09465] Fps is (10 sec: 3277.3, 60 sec: 3686.7, 300 sec: 3693.3). Total num frames: 1757184. Throughput: 0: 904.8. Samples: 437162. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:06:58,469][09465] Avg episode reward: [(0, '7.252')]
[2023-02-25 17:06:58,479][15469] Saving new best policy, reward=7.252!
[2023-02-25 17:06:58,907][15488] Updated weights for policy 0, policy_version 430 (0.0015)
[2023-02-25 17:07:03,468][09465] Fps is (10 sec: 4505.5, 60 sec: 3686.4, 300 sec: 3735.0). Total num frames: 1781760. Throughput: 0: 940.8. Samples: 444020. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:07:03,470][09465] Avg episode reward: [(0, '7.221')]
[2023-02-25 17:07:08,468][09465] Fps is (10 sec: 4095.9, 60 sec: 3686.7, 300 sec: 3735.0). Total num frames: 1798144. Throughput: 0: 926.6. Samples: 449984. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:07:08,472][09465] Avg episode reward: [(0, '7.089')]
[2023-02-25 17:07:09,042][15488] Updated weights for policy 0, policy_version 440 (0.0014)
[2023-02-25 17:07:13,467][09465] Fps is (10 sec: 3276.9, 60 sec: 3754.7, 300 sec: 3721.1). Total num frames: 1814528. Throughput: 0: 909.8. Samples: 452198. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:07:13,473][09465] Avg episode reward: [(0, '7.168')]
[2023-02-25 17:07:18,467][09465] Fps is (10 sec: 3276.9, 60 sec: 3686.4, 300 sec: 3707.2). Total num frames: 1830912. Throughput: 0: 924.3. Samples: 457086. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:07:18,471][09465] Avg episode reward: [(0, '7.585')]
[2023-02-25 17:07:18,579][15469] Saving new best policy, reward=7.585!
[2023-02-25 17:07:20,414][15488] Updated weights for policy 0, policy_version 450 (0.0029)
[2023-02-25 17:07:23,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3735.0). Total num frames: 1855488. Throughput: 0: 953.6. Samples: 463918. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:07:23,470][09465] Avg episode reward: [(0, '7.435')]
[2023-02-25 17:07:28,471][09465] Fps is (10 sec: 4094.5, 60 sec: 3754.7, 300 sec: 3735.0). Total num frames: 1871872. Throughput: 0: 951.0. Samples: 467306. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:07:28,473][09465] Avg episode reward: [(0, '7.962')]
[2023-02-25 17:07:28,485][15469] Saving new best policy, reward=7.962!
[2023-02-25 17:07:31,667][15488] Updated weights for policy 0, policy_version 460 (0.0019)
[2023-02-25 17:07:33,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3707.2). Total num frames: 1888256. Throughput: 0: 910.5. Samples: 471524. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:07:33,473][09465] Avg episode reward: [(0, '8.425')]
[2023-02-25 17:07:33,478][15469] Saving new best policy, reward=8.425!
[2023-02-25 17:07:38,467][09465] Fps is (10 sec: 3278.0, 60 sec: 3686.4, 300 sec: 3707.2). Total num frames: 1904640. Throughput: 0: 933.0. Samples: 476786. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:07:38,472][09465] Avg episode reward: [(0, '8.785')]
[2023-02-25 17:07:38,505][15469] Saving new best policy, reward=8.785!
[2023-02-25 17:07:42,123][15488] Updated weights for policy 0, policy_version 470 (0.0022)
[2023-02-25 17:07:43,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3735.0). Total num frames: 1929216. Throughput: 0: 955.7. Samples: 480170. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:07:43,478][09465] Avg episode reward: [(0, '9.398')]
[2023-02-25 17:07:43,483][15469] Saving new best policy, reward=9.398!
[2023-02-25 17:07:48,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3686.5, 300 sec: 3735.0). Total num frames: 1945600. Throughput: 0: 945.1. Samples: 486550. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:07:48,476][09465] Avg episode reward: [(0, '9.396')]
[2023-02-25 17:07:53,468][09465] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3707.2). Total num frames: 1961984. Throughput: 0: 908.4. Samples: 490860. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:07:53,475][09465] Avg episode reward: [(0, '9.276')]
[2023-02-25 17:07:54,511][15488] Updated weights for policy 0, policy_version 480 (0.0024)
[2023-02-25 17:07:58,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3707.3). Total num frames: 1982464. Throughput: 0: 908.1. Samples: 493062. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:07:58,475][09465] Avg episode reward: [(0, '9.415')]
[2023-02-25 17:07:58,485][15469] Saving new best policy, reward=9.415!
[2023-02-25 17:08:03,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3735.1). Total num frames: 2002944. Throughput: 0: 949.3. Samples: 499806. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:08:03,470][09465] Avg episode reward: [(0, '9.087')]
[2023-02-25 17:08:03,688][15488] Updated weights for policy 0, policy_version 490 (0.0028)
[2023-02-25 17:08:08,470][09465] Fps is (10 sec: 4094.8, 60 sec: 3754.5, 300 sec: 3748.8). Total num frames: 2023424. Throughput: 0: 934.0. Samples: 505950. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:08:08,477][09465] Avg episode reward: [(0, '9.710')]
[2023-02-25 17:08:08,488][15469] Saving new best policy, reward=9.710!
[2023-02-25 17:08:13,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3686.4, 300 sec: 3707.2). Total num frames: 2035712. Throughput: 0: 906.9. Samples: 508114. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:08:13,477][09465] Avg episode reward: [(0, '9.267')]
[2023-02-25 17:08:16,397][15488] Updated weights for policy 0, policy_version 500 (0.0011)
[2023-02-25 17:08:18,468][09465] Fps is (10 sec: 3277.7, 60 sec: 3754.7, 300 sec: 3707.2). Total num frames: 2056192. Throughput: 0: 916.4. Samples: 512764. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:08:18,470][09465] Avg episode reward: [(0, '9.584')]
[2023-02-25 17:08:18,483][15469] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000502_2056192.pth...
[2023-02-25 17:08:18,598][15469] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000284_1163264.pth
[2023-02-25 17:08:23,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3721.1). Total num frames: 2076672. Throughput: 0: 940.9. Samples: 519128. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:08:23,470][09465] Avg episode reward: [(0, '9.950')]
[2023-02-25 17:08:23,477][15469] Saving new best policy, reward=9.950!
[2023-02-25 17:08:28,035][15488] Updated weights for policy 0, policy_version 510 (0.0012)
[2023-02-25 17:08:28,472][09465] Fps is (10 sec: 3275.3, 60 sec: 3618.1, 300 sec: 3707.2). Total num frames: 2088960. Throughput: 0: 912.9. Samples: 521254. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:08:28,475][09465] Avg episode reward: [(0, '10.072')]
[2023-02-25 17:08:28,491][15469] Saving new best policy, reward=10.072!
[2023-02-25 17:08:33,469][09465] Fps is (10 sec: 2457.2, 60 sec: 3549.8, 300 sec: 3679.4). Total num frames: 2101248. Throughput: 0: 849.5. Samples: 524780. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:08:33,473][09465] Avg episode reward: [(0, '11.722')]
[2023-02-25 17:08:33,481][15469] Saving new best policy, reward=11.722!
[2023-02-25 17:08:38,468][09465] Fps is (10 sec: 2458.7, 60 sec: 3481.6, 300 sec: 3651.7). Total num frames: 2113536. Throughput: 0: 844.2. Samples: 528848. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:08:38,470][09465] Avg episode reward: [(0, '11.806')]
[2023-02-25 17:08:38,488][15469] Saving new best policy, reward=11.806!
[2023-02-25 17:08:41,864][15488] Updated weights for policy 0, policy_version 520 (0.0044)
[2023-02-25 17:08:43,467][09465] Fps is (10 sec: 3277.4, 60 sec: 3413.3, 300 sec: 3665.6). Total num frames: 2134016. Throughput: 0: 856.1. Samples: 531588. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:08:43,472][09465] Avg episode reward: [(0, '12.059')]
[2023-02-25 17:08:43,477][15469] Saving new best policy, reward=12.059!
[2023-02-25 17:08:48,467][09465] Fps is (10 sec: 4505.7, 60 sec: 3549.9, 300 sec: 3693.3). Total num frames: 2158592. Throughput: 0: 859.0. Samples: 538462. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:08:48,469][09465] Avg episode reward: [(0, '11.752')]
[2023-02-25 17:08:51,703][15488] Updated weights for policy 0, policy_version 530 (0.0012)
[2023-02-25 17:08:53,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3549.9, 300 sec: 3679.5). Total num frames: 2174976. Throughput: 0: 844.8. Samples: 543964. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-25 17:08:53,470][09465] Avg episode reward: [(0, '11.165')]
[2023-02-25 17:08:58,467][09465] Fps is (10 sec: 2867.2, 60 sec: 3413.3, 300 sec: 3651.7). Total num frames: 2187264. Throughput: 0: 845.8. Samples: 546176. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:08:58,471][09465] Avg episode reward: [(0, '11.503')]
[2023-02-25 17:09:03,441][15488] Updated weights for policy 0, policy_version 540 (0.0012)
[2023-02-25 17:09:03,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3481.6, 300 sec: 3679.5). Total num frames: 2211840. Throughput: 0: 860.0. Samples: 551464. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:09:03,471][09465] Avg episode reward: [(0, '12.495')]
[2023-02-25 17:09:03,473][15469] Saving new best policy, reward=12.495!
[2023-02-25 17:09:08,468][09465] Fps is (10 sec: 4505.5, 60 sec: 3481.8, 300 sec: 3693.3). Total num frames: 2232320. Throughput: 0: 872.5. Samples: 558392. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:09:08,470][09465] Avg episode reward: [(0, '12.536')]
[2023-02-25 17:09:08,484][15469] Saving new best policy, reward=12.536!
[2023-02-25 17:09:13,468][09465] Fps is (10 sec: 3686.1, 60 sec: 3549.8, 300 sec: 3679.5). Total num frames: 2248704. Throughput: 0: 892.5. Samples: 561414. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:09:13,473][09465] Avg episode reward: [(0, '13.304')]
[2023-02-25 17:09:13,478][15469] Saving new best policy, reward=13.304!
[2023-02-25 17:09:13,925][15488] Updated weights for policy 0, policy_version 550 (0.0021)
[2023-02-25 17:09:18,468][09465] Fps is (10 sec: 3276.8, 60 sec: 3481.6, 300 sec: 3651.7). Total num frames: 2265088. Throughput: 0: 910.2. Samples: 565736. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:09:18,475][09465] Avg episode reward: [(0, '13.103')]
[2023-02-25 17:09:23,467][09465] Fps is (10 sec: 3686.7, 60 sec: 3481.6, 300 sec: 3679.5). Total num frames: 2285568. Throughput: 0: 942.5. Samples: 571262. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:09:23,475][09465] Avg episode reward: [(0, '13.039')]
[2023-02-25 17:09:24,972][15488] Updated weights for policy 0, policy_version 560 (0.0037)
[2023-02-25 17:09:28,468][09465] Fps is (10 sec: 4505.5, 60 sec: 3686.7, 300 sec: 3707.2). Total num frames: 2310144. Throughput: 0: 960.5. Samples: 574812. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:09:28,470][09465] Avg episode reward: [(0, '13.135')]
[2023-02-25 17:09:33,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3754.8, 300 sec: 3693.3). Total num frames: 2326528. Throughput: 0: 946.4. Samples: 581050. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:09:33,472][09465] Avg episode reward: [(0, '13.266')]
[2023-02-25 17:09:36,097][15488] Updated weights for policy 0, policy_version 570 (0.0013)
[2023-02-25 17:09:38,467][09465] Fps is (10 sec: 2867.3, 60 sec: 3754.7, 300 sec: 3651.8). Total num frames: 2338816. Throughput: 0: 920.8. Samples: 585398. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-25 17:09:38,473][09465] Avg episode reward: [(0, '13.612')]
[2023-02-25 17:09:38,489][15469] Saving new best policy, reward=13.612!
[2023-02-25 17:09:43,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3679.5). Total num frames: 2359296. Throughput: 0: 926.8. Samples: 587880. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:09:43,470][09465] Avg episode reward: [(0, '13.825')]
[2023-02-25 17:09:43,474][15469] Saving new best policy, reward=13.825!
[2023-02-25 17:09:46,250][15488] Updated weights for policy 0, policy_version 580 (0.0019)
[2023-02-25 17:09:48,467][09465] Fps is (10 sec: 4505.6, 60 sec: 3754.7, 300 sec: 3707.2). Total num frames: 2383872. Throughput: 0: 965.0. Samples: 594888. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:09:48,469][09465] Avg episode reward: [(0, '13.959')]
[2023-02-25 17:09:48,482][15469] Saving new best policy, reward=13.959!
[2023-02-25 17:09:53,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3679.5). Total num frames: 2400256. Throughput: 0: 939.1. Samples: 600652. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:09:53,473][09465] Avg episode reward: [(0, '14.629')]
[2023-02-25 17:09:53,479][15469] Saving new best policy, reward=14.629!
[2023-02-25 17:09:58,412][15488] Updated weights for policy 0, policy_version 590 (0.0011)
[2023-02-25 17:09:58,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3665.6). Total num frames: 2416640. Throughput: 0: 920.6. Samples: 602840. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:09:58,474][09465] Avg episode reward: [(0, '15.537')]
[2023-02-25 17:09:58,487][15469] Saving new best policy, reward=15.537!
[2023-02-25 17:10:03,468][09465] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3679.5). Total num frames: 2437120. Throughput: 0: 936.9. Samples: 607896. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:10:03,470][09465] Avg episode reward: [(0, '16.453')]
[2023-02-25 17:10:03,473][15469] Saving new best policy, reward=16.453!
[2023-02-25 17:10:07,743][15488] Updated weights for policy 0, policy_version 600 (0.0017)
[2023-02-25 17:10:08,468][09465] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3707.2). Total num frames: 2457600. Throughput: 0: 971.6. Samples: 614984. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:10:08,469][09465] Avg episode reward: [(0, '16.839')]
[2023-02-25 17:10:08,490][15469] Saving new best policy, reward=16.839!
[2023-02-25 17:10:13,469][09465] Fps is (10 sec: 4095.4, 60 sec: 3822.9, 300 sec: 3693.3). Total num frames: 2478080. Throughput: 0: 962.8. Samples: 618138. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:10:13,477][09465] Avg episode reward: [(0, '18.144')]
[2023-02-25 17:10:13,479][15469] Saving new best policy, reward=18.144!
[2023-02-25 17:10:18,468][09465] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3665.6). Total num frames: 2490368. Throughput: 0: 921.2. Samples: 622504. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:10:18,475][09465] Avg episode reward: [(0, '17.902')]
[2023-02-25 17:10:18,488][15469] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000608_2490368.pth...
[2023-02-25 17:10:18,634][15469] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000393_1609728.pth
[2023-02-25 17:10:20,065][15488] Updated weights for policy 0, policy_version 610 (0.0029)
[2023-02-25 17:10:23,467][09465] Fps is (10 sec: 3277.3, 60 sec: 3754.7, 300 sec: 3679.5). Total num frames: 2510848. Throughput: 0: 950.2. Samples: 628156. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:10:23,474][09465] Avg episode reward: [(0, '17.738')]
[2023-02-25 17:10:28,467][09465] Fps is (10 sec: 4505.7, 60 sec: 3754.7, 300 sec: 3721.1). Total num frames: 2535424. Throughput: 0: 971.4. Samples: 631592. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:10:28,470][09465] Avg episode reward: [(0, '17.817')]
[2023-02-25 17:10:28,731][15488] Updated weights for policy 0, policy_version 620 (0.0012)
[2023-02-25 17:10:33,470][09465] Fps is (10 sec: 4095.1, 60 sec: 3754.5, 300 sec: 3693.3). Total num frames: 2551808. Throughput: 0: 956.2. Samples: 637918. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:10:33,475][09465] Avg episode reward: [(0, '17.120')]
[2023-02-25 17:10:38,468][09465] Fps is (10 sec: 3276.7, 60 sec: 3822.9, 300 sec: 3679.5). Total num frames: 2568192. Throughput: 0: 925.0. Samples: 642276. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:10:38,470][09465] Avg episode reward: [(0, '18.184')]
[2023-02-25 17:10:38,486][15469] Saving new best policy, reward=18.184!
[2023-02-25 17:10:41,210][15488] Updated weights for policy 0, policy_version 630 (0.0032)
[2023-02-25 17:10:43,467][09465] Fps is (10 sec: 3687.2, 60 sec: 3822.9, 300 sec: 3693.3). Total num frames: 2588672. Throughput: 0: 934.1. Samples: 644876. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:10:43,470][09465] Avg episode reward: [(0, '17.322')]
[2023-02-25 17:10:48,467][09465] Fps is (10 sec: 4505.8, 60 sec: 3822.9, 300 sec: 3721.1). Total num frames: 2613248. Throughput: 0: 978.9. Samples: 651948. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:10:48,478][09465] Avg episode reward: [(0, '18.109')]
[2023-02-25 17:10:50,229][15488] Updated weights for policy 0, policy_version 640 (0.0013)
[2023-02-25 17:10:53,471][09465] Fps is (10 sec: 4094.7, 60 sec: 3822.7, 300 sec: 3707.2). Total num frames: 2629632. Throughput: 0: 950.5. Samples: 657760. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:10:53,479][09465] Avg episode reward: [(0, '19.500')]
[2023-02-25 17:10:53,482][15469] Saving new best policy, reward=19.500!
[2023-02-25 17:10:58,468][09465] Fps is (10 sec: 2867.1, 60 sec: 3754.7, 300 sec: 3665.6). Total num frames: 2641920. Throughput: 0: 928.5. Samples: 659918. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:10:58,473][09465] Avg episode reward: [(0, '20.781')]
[2023-02-25 17:10:58,494][15469] Saving new best policy, reward=20.781!
[2023-02-25 17:11:02,433][15488] Updated weights for policy 0, policy_version 650 (0.0021)
[2023-02-25 17:11:03,467][09465] Fps is (10 sec: 3687.6, 60 sec: 3822.9, 300 sec: 3693.4). Total num frames: 2666496. Throughput: 0: 947.8. Samples: 665154. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:11:03,475][09465] Avg episode reward: [(0, '20.914')]
[2023-02-25 17:11:03,480][15469] Saving new best policy, reward=20.914!
[2023-02-25 17:11:08,467][09465] Fps is (10 sec: 4505.7, 60 sec: 3822.9, 300 sec: 3721.1). Total num frames: 2686976. Throughput: 0: 978.1. Samples: 672172. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:11:08,469][09465] Avg episode reward: [(0, '20.973')]
[2023-02-25 17:11:08,541][15469] Saving new best policy, reward=20.973!
[2023-02-25 17:11:12,071][15488] Updated weights for policy 0, policy_version 660 (0.0016)
[2023-02-25 17:11:13,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3823.0, 300 sec: 3721.1). Total num frames: 2707456. Throughput: 0: 970.4. Samples: 675260. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-25 17:11:13,472][09465] Avg episode reward: [(0, '18.575')]
[2023-02-25 17:11:18,472][09465] Fps is (10 sec: 3275.4, 60 sec: 3822.7, 300 sec: 3679.4). Total num frames: 2719744. Throughput: 0: 929.4. Samples: 679744. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:11:18,475][09465] Avg episode reward: [(0, '17.739')]
[2023-02-25 17:11:23,457][15488] Updated weights for policy 0, policy_version 670 (0.0014)
[2023-02-25 17:11:23,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3891.2, 300 sec: 3721.2). Total num frames: 2744320. Throughput: 0: 959.9. Samples: 685470. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-25 17:11:23,469][09465] Avg episode reward: [(0, '17.645')]
[2023-02-25 17:11:28,468][09465] Fps is (10 sec: 4507.5, 60 sec: 3822.9, 300 sec: 3735.0). Total num frames: 2764800. Throughput: 0: 978.7. Samples: 688916. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:11:28,470][09465] Avg episode reward: [(0, '17.886')]
[2023-02-25 17:11:33,471][09465] Fps is (10 sec: 3685.1, 60 sec: 3822.8, 300 sec: 3721.1). Total num frames: 2781184. Throughput: 0: 957.7. Samples: 695050. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-25 17:11:33,474][09465] Avg episode reward: [(0, '17.186')]
[2023-02-25 17:11:34,065][15488] Updated weights for policy 0, policy_version 680 (0.0012)
[2023-02-25 17:11:38,468][09465] Fps is (10 sec: 3276.6, 60 sec: 3822.9, 300 sec: 3693.3). Total num frames: 2797568. Throughput: 0: 926.3. Samples: 699440. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-25 17:11:38,471][09465] Avg episode reward: [(0, '18.185')]
[2023-02-25 17:11:43,467][09465] Fps is (10 sec: 3687.8, 60 sec: 3822.9, 300 sec: 3707.2). Total num frames: 2818048. Throughput: 0: 938.5. Samples: 702150. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:11:43,474][09465] Avg episode reward: [(0, '18.889')]
[2023-02-25 17:11:44,847][15488] Updated weights for policy 0, policy_version 690 (0.0016)
[2023-02-25 17:11:48,467][09465] Fps is (10 sec: 4505.9, 60 sec: 3822.9, 300 sec: 3748.9). Total num frames: 2842624. Throughput: 0: 979.6. Samples: 709234. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:11:48,469][09465] Avg episode reward: [(0, '17.040')]
[2023-02-25 17:11:53,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3823.1, 300 sec: 3735.0). Total num frames: 2859008. Throughput: 0: 953.9. Samples: 715096. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:11:53,470][09465] Avg episode reward: [(0, '16.800')]
[2023-02-25 17:11:55,824][15488] Updated weights for policy 0, policy_version 700 (0.0045)
[2023-02-25 17:11:58,468][09465] Fps is (10 sec: 2867.1, 60 sec: 3822.9, 300 sec: 3693.3). Total num frames: 2871296. Throughput: 0: 934.0. Samples: 717292. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:11:58,473][09465] Avg episode reward: [(0, '16.995')]
[2023-02-25 17:12:03,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3721.1). Total num frames: 2895872. Throughput: 0: 951.6. Samples: 722560. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:12:03,470][09465] Avg episode reward: [(0, '17.150')]
[2023-02-25 17:12:05,975][15488] Updated weights for policy 0, policy_version 710 (0.0012)
[2023-02-25 17:12:08,468][09465] Fps is (10 sec: 4915.3, 60 sec: 3891.2, 300 sec: 3748.9). Total num frames: 2920448. Throughput: 0: 980.6. Samples: 729598. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:12:08,470][09465] Avg episode reward: [(0, '17.308')]
[2023-02-25 17:12:13,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3748.9). Total num frames: 2936832. Throughput: 0: 974.4. Samples: 732764. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:12:13,470][09465] Avg episode reward: [(0, '17.838')]
[2023-02-25 17:12:17,288][15488] Updated weights for policy 0, policy_version 720 (0.0027)
[2023-02-25 17:12:18,470][09465] Fps is (10 sec: 2866.5, 60 sec: 3823.1, 300 sec: 3707.2). Total num frames: 2949120. Throughput: 0: 936.9. Samples: 737210. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:12:18,479][09465] Avg episode reward: [(0, '19.016')]
[2023-02-25 17:12:18,487][15469] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000720_2949120.pth...
[2023-02-25 17:12:18,654][15469] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000502_2056192.pth
[2023-02-25 17:12:23,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3735.0). Total num frames: 2973696. Throughput: 0: 967.6. Samples: 742980. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:12:23,472][09465] Avg episode reward: [(0, '18.660')]
[2023-02-25 17:12:26,889][15488] Updated weights for policy 0, policy_version 730 (0.0018)
[2023-02-25 17:12:28,468][09465] Fps is (10 sec: 4506.7, 60 sec: 3822.9, 300 sec: 3748.9). Total num frames: 2994176. Throughput: 0: 986.8. Samples: 746558. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:12:28,470][09465] Avg episode reward: [(0, '18.404')]
[2023-02-25 17:12:33,470][09465] Fps is (10 sec: 3685.4, 60 sec: 3823.0, 300 sec: 3748.8). Total num frames: 3010560. Throughput: 0: 969.5. Samples: 752864. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:12:33,475][09465] Avg episode reward: [(0, '19.132')]
[2023-02-25 17:12:38,468][09465] Fps is (10 sec: 3276.6, 60 sec: 3822.9, 300 sec: 3721.1). Total num frames: 3026944. Throughput: 0: 938.0. Samples: 757306. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:12:38,476][09465] Avg episode reward: [(0, '19.065')]
[2023-02-25 17:12:38,996][15488] Updated weights for policy 0, policy_version 740 (0.0037)
[2023-02-25 17:12:43,467][09465] Fps is (10 sec: 3687.3, 60 sec: 3822.9, 300 sec: 3735.0). Total num frames: 3047424. Throughput: 0: 947.6. Samples: 759934. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:12:43,469][09465] Avg episode reward: [(0, '18.770')]
[2023-02-25 17:12:47,983][15488] Updated weights for policy 0, policy_version 750 (0.0012)
[2023-02-25 17:12:48,467][09465] Fps is (10 sec: 4505.8, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3072000. Throughput: 0: 986.1. Samples: 766936. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:12:48,479][09465] Avg episode reward: [(0, '20.505')]
[2023-02-25 17:12:53,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3748.9). Total num frames: 3088384. Throughput: 0: 957.7. Samples: 772694. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:12:53,470][09465] Avg episode reward: [(0, '20.894')]
[2023-02-25 17:12:58,468][09465] Fps is (10 sec: 3276.7, 60 sec: 3891.2, 300 sec: 3735.0). Total num frames: 3104768. Throughput: 0: 932.2. Samples: 774712. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:12:58,476][09465] Avg episode reward: [(0, '21.343')]
[2023-02-25 17:12:58,492][15469] Saving new best policy, reward=21.343!
[2023-02-25 17:13:00,504][15488] Updated weights for policy 0, policy_version 760 (0.0012)
[2023-02-25 17:13:03,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3735.0). Total num frames: 3125248. Throughput: 0: 950.1. Samples: 779964. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:13:03,475][09465] Avg episode reward: [(0, '22.085')]
[2023-02-25 17:13:03,480][15469] Saving new best policy, reward=22.085!
[2023-02-25 17:13:08,467][09465] Fps is (10 sec: 4096.1, 60 sec: 3754.7, 300 sec: 3762.8). Total num frames: 3145728. Throughput: 0: 976.6. Samples: 786928. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:13:08,475][09465] Avg episode reward: [(0, '22.110')]
[2023-02-25 17:13:08,486][15469] Saving new best policy, reward=22.110!
[2023-02-25 17:13:09,573][15488] Updated weights for policy 0, policy_version 770 (0.0021)
[2023-02-25 17:13:13,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3748.9). Total num frames: 3162112. Throughput: 0: 964.4. Samples: 789958. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:13:13,471][09465] Avg episode reward: [(0, '21.364')]
[2023-02-25 17:13:18,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3823.1, 300 sec: 3735.0). Total num frames: 3178496. Throughput: 0: 922.2. Samples: 794362. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:13:18,469][09465] Avg episode reward: [(0, '21.104')]
[2023-02-25 17:13:21,828][15488] Updated weights for policy 0, policy_version 780 (0.0031)
[2023-02-25 17:13:23,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3762.8). Total num frames: 3198976. Throughput: 0: 951.5. Samples: 800124. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:13:23,477][09465] Avg episode reward: [(0, '22.103')]
[2023-02-25 17:13:28,468][09465] Fps is (10 sec: 4505.5, 60 sec: 3822.9, 300 sec: 3804.4). Total num frames: 3223552. Throughput: 0: 969.8. Samples: 803576. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:13:28,475][09465] Avg episode reward: [(0, '21.596')]
[2023-02-25 17:13:31,197][15488] Updated weights for policy 0, policy_version 790 (0.0017)
[2023-02-25 17:13:33,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3823.1, 300 sec: 3818.3). Total num frames: 3239936. Throughput: 0: 952.0. Samples: 809774. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:13:33,470][09465] Avg episode reward: [(0, '22.114')]
[2023-02-25 17:13:33,481][15469] Saving new best policy, reward=22.114!
[2023-02-25 17:13:38,468][09465] Fps is (10 sec: 3276.9, 60 sec: 3823.0, 300 sec: 3804.4). Total num frames: 3256320. Throughput: 0: 920.3. Samples: 814108. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:13:38,470][09465] Avg episode reward: [(0, '22.863')]
[2023-02-25 17:13:38,487][15469] Saving new best policy, reward=22.863!
[2023-02-25 17:13:43,310][15488] Updated weights for policy 0, policy_version 800 (0.0026)
[2023-02-25 17:13:43,468][09465] Fps is (10 sec: 3686.3, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 3276800. Throughput: 0: 930.3. Samples: 816574. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:13:43,470][09465] Avg episode reward: [(0, '21.353')]
[2023-02-25 17:13:48,468][09465] Fps is (10 sec: 4095.9, 60 sec: 3754.6, 300 sec: 3804.4). Total num frames: 3297280. Throughput: 0: 969.8. Samples: 823606. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
[2023-02-25 17:13:48,471][09465] Avg episode reward: [(0, '19.426')]
[2023-02-25 17:13:53,187][15488] Updated weights for policy 0, policy_version 810 (0.0027)
[2023-02-25 17:13:53,467][09465] Fps is (10 sec: 4096.1, 60 sec: 3822.9, 300 sec: 3832.2). Total num frames: 3317760. Throughput: 0: 946.1. Samples: 829502. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:13:53,475][09465] Avg episode reward: [(0, '18.391')]
[2023-02-25 17:13:58,467][09465] Fps is (10 sec: 3276.9, 60 sec: 3754.7, 300 sec: 3790.5). Total num frames: 3330048. Throughput: 0: 926.8. Samples: 831666. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:13:58,473][09465] Avg episode reward: [(0, '17.930')]
[2023-02-25 17:14:03,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3790.5). Total num frames: 3350528. Throughput: 0: 940.0. Samples: 836660. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:14:03,476][09465] Avg episode reward: [(0, '16.992')]
[2023-02-25 17:14:04,696][15488] Updated weights for policy 0, policy_version 820 (0.0012)
[2023-02-25 17:14:08,467][09465] Fps is (10 sec: 4505.6, 60 sec: 3822.9, 300 sec: 3818.3). Total num frames: 3375104. Throughput: 0: 965.6. Samples: 843578. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:14:08,475][09465] Avg episode reward: [(0, '18.370')]
[2023-02-25 17:14:13,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3818.3). Total num frames: 3391488. Throughput: 0: 960.9. Samples: 846816. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:14:13,473][09465] Avg episode reward: [(0, '19.511')]
[2023-02-25 17:14:15,768][15488] Updated weights for policy 0, policy_version 830 (0.0012)
[2023-02-25 17:14:18,468][09465] Fps is (10 sec: 2867.1, 60 sec: 3754.6, 300 sec: 3790.5). Total num frames: 3403776. Throughput: 0: 920.5. Samples: 851198. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:14:18,474][09465] Avg episode reward: [(0, '19.352')]
[2023-02-25 17:14:18,491][15469] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000831_3403776.pth...
[2023-02-25 17:14:18,638][15469] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000608_2490368.pth
[2023-02-25 17:14:23,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 3428352. Throughput: 0: 944.9. Samples: 856628. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:14:23,470][09465] Avg episode reward: [(0, '21.008')]
[2023-02-25 17:14:26,003][15488] Updated weights for policy 0, policy_version 840 (0.0019)
[2023-02-25 17:14:28,467][09465] Fps is (10 sec: 4505.8, 60 sec: 3754.7, 300 sec: 3804.4). Total num frames: 3448832. Throughput: 0: 968.5. Samples: 860156. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:14:28,471][09465] Avg episode reward: [(0, '21.470')]
[2023-02-25 17:14:33,469][09465] Fps is (10 sec: 4095.4, 60 sec: 3822.8, 300 sec: 3832.2). Total num frames: 3469312. Throughput: 0: 954.1. Samples: 866542. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:14:33,472][09465] Avg episode reward: [(0, '22.169')]
[2023-02-25 17:14:38,309][15488] Updated weights for policy 0, policy_version 850 (0.0021)
[2023-02-25 17:14:38,467][09465] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3804.4). Total num frames: 3481600. Throughput: 0: 911.2. Samples: 870506. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:14:38,475][09465] Avg episode reward: [(0, '22.163')]
[2023-02-25 17:14:43,467][09465] Fps is (10 sec: 2458.0, 60 sec: 3618.1, 300 sec: 3762.8). Total num frames: 3493888. Throughput: 0: 902.2. Samples: 872266. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-25 17:14:43,474][09465] Avg episode reward: [(0, '21.649')]
[2023-02-25 17:14:48,467][09465] Fps is (10 sec: 2867.2, 60 sec: 3549.9, 300 sec: 3762.8). Total num frames: 3510272. Throughput: 0: 881.8. Samples: 876342. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-25 17:14:48,474][09465] Avg episode reward: [(0, '23.016')]
[2023-02-25 17:14:48,487][15469] Saving new best policy, reward=23.016!
[2023-02-25 17:14:50,890][15488] Updated weights for policy 0, policy_version 860 (0.0027)
[2023-02-25 17:14:53,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3776.7). Total num frames: 3530752. Throughput: 0: 878.7. Samples: 883120. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-25 17:14:53,470][09465] Avg episode reward: [(0, '24.598')]
[2023-02-25 17:14:53,477][15469] Saving new best policy, reward=24.598!
[2023-02-25 17:14:58,469][09465] Fps is (10 sec: 3685.8, 60 sec: 3618.0, 300 sec: 3762.7). Total num frames: 3547136. Throughput: 0: 860.2. Samples: 885526. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:14:58,472][09465] Avg episode reward: [(0, '24.473')]
[2023-02-25 17:15:03,467][09465] Fps is (10 sec: 2867.2, 60 sec: 3481.6, 300 sec: 3735.0). Total num frames: 3559424. Throughput: 0: 861.5. Samples: 889966. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:15:03,470][09465] Avg episode reward: [(0, '24.125')]
[2023-02-25 17:15:03,571][15488] Updated weights for policy 0, policy_version 870 (0.0027)
[2023-02-25 17:15:08,467][09465] Fps is (10 sec: 3687.0, 60 sec: 3481.6, 300 sec: 3748.9). Total num frames: 3584000. Throughput: 0: 883.6. Samples: 896388. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:15:08,472][09465] Avg episode reward: [(0, '23.174')]
[2023-02-25 17:15:12,125][15488] Updated weights for policy 0, policy_version 880 (0.0014)
[2023-02-25 17:15:13,472][09465] Fps is (10 sec: 4913.2, 60 sec: 3617.9, 300 sec: 3790.5). Total num frames: 3608576. Throughput: 0: 884.6. Samples: 899968. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:15:13,474][09465] Avg episode reward: [(0, '23.086')]
[2023-02-25 17:15:18,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3776.7). Total num frames: 3624960. Throughput: 0: 868.9. Samples: 905640. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:15:18,471][09465] Avg episode reward: [(0, '22.426')]
[2023-02-25 17:15:23,467][09465] Fps is (10 sec: 2868.4, 60 sec: 3481.6, 300 sec: 3735.0). Total num frames: 3637248. Throughput: 0: 879.1. Samples: 910066. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:15:23,475][09465] Avg episode reward: [(0, '21.633')]
[2023-02-25 17:15:24,609][15488] Updated weights for policy 0, policy_version 890 (0.0012)
[2023-02-25 17:15:28,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3549.9, 300 sec: 3762.8). Total num frames: 3661824. Throughput: 0: 909.0. Samples: 913170. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:15:28,474][09465] Avg episode reward: [(0, '22.108')]
[2023-02-25 17:15:33,097][15488] Updated weights for policy 0, policy_version 900 (0.0032)
[2023-02-25 17:15:33,467][09465] Fps is (10 sec: 4915.2, 60 sec: 3618.2, 300 sec: 3790.5). Total num frames: 3686400. Throughput: 0: 977.5. Samples: 920330. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-25 17:15:33,475][09465] Avg episode reward: [(0, '23.413')]
[2023-02-25 17:15:38,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3618.1, 300 sec: 3762.8). Total num frames: 3698688. Throughput: 0: 939.8. Samples: 925412. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:15:38,470][09465] Avg episode reward: [(0, '23.967')]
[2023-02-25 17:15:43,467][09465] Fps is (10 sec: 2867.2, 60 sec: 3686.4, 300 sec: 3735.0). Total num frames: 3715072. Throughput: 0: 934.2. Samples: 927564. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:15:43,470][09465] Avg episode reward: [(0, '24.214')]
[2023-02-25 17:15:45,672][15488] Updated weights for policy 0, policy_version 910 (0.0034)
[2023-02-25 17:15:48,467][09465] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3739648. Throughput: 0: 966.2. Samples: 933444. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:15:48,476][09465] Avg episode reward: [(0, '23.992')]
[2023-02-25 17:15:53,468][09465] Fps is (10 sec: 4505.3, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 3760128. Throughput: 0: 979.4. Samples: 940462. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:15:53,472][09465] Avg episode reward: [(0, '23.335')]
[2023-02-25 17:15:55,018][15488] Updated weights for policy 0, policy_version 920 (0.0019)
[2023-02-25 17:15:58,472][09465] Fps is (10 sec: 3684.7, 60 sec: 3822.7, 300 sec: 3762.7). Total num frames: 3776512. Throughput: 0: 957.5. Samples: 943058. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:15:58,475][09465] Avg episode reward: [(0, '22.098')]
[2023-02-25 17:16:03,467][09465] Fps is (10 sec: 3277.0, 60 sec: 3891.2, 300 sec: 3748.9). Total num frames: 3792896. Throughput: 0: 928.9. Samples: 947442. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:16:03,471][09465] Avg episode reward: [(0, '20.586')]
[2023-02-25 17:16:06,929][15488] Updated weights for policy 0, policy_version 930 (0.0030)
[2023-02-25 17:16:08,467][09465] Fps is (10 sec: 3688.1, 60 sec: 3822.9, 300 sec: 3748.9). Total num frames: 3813376. Throughput: 0: 971.7. Samples: 953794. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:16:08,469][09465] Avg episode reward: [(0, '20.191')]
[2023-02-25 17:16:13,467][09465] Fps is (10 sec: 4505.6, 60 sec: 3823.2, 300 sec: 3790.6). Total num frames: 3837952. Throughput: 0: 980.7. Samples: 957300. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:16:13,474][09465] Avg episode reward: [(0, '19.938')]
[2023-02-25 17:16:16,886][15488] Updated weights for policy 0, policy_version 940 (0.0015)
[2023-02-25 17:16:18,468][09465] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3854336. Throughput: 0: 948.0. Samples: 962990. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-25 17:16:18,476][09465] Avg episode reward: [(0, '20.364')]
[2023-02-25 17:16:18,490][15469] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000941_3854336.pth...
[2023-02-25 17:16:18,629][15469] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000720_2949120.pth
[2023-02-25 17:16:23,470][09465] Fps is (10 sec: 2866.5, 60 sec: 3822.8, 300 sec: 3735.0). Total num frames: 3866624. Throughput: 0: 933.1. Samples: 967404. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:16:23,472][09465] Avg episode reward: [(0, '20.596')]
[2023-02-25 17:16:27,868][15488] Updated weights for policy 0, policy_version 950 (0.0031)
[2023-02-25 17:16:28,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3891200. Throughput: 0: 956.1. Samples: 970590. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:16:28,475][09465] Avg episode reward: [(0, '22.484')]
[2023-02-25 17:16:33,471][09465] Fps is (10 sec: 4505.2, 60 sec: 3754.4, 300 sec: 3776.6). Total num frames: 3911680. Throughput: 0: 976.7. Samples: 977400. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-25 17:16:33,479][09465] Avg episode reward: [(0, '22.568')]
[2023-02-25 17:16:38,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3928064. Throughput: 0: 931.7. Samples: 982388. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:16:38,473][09465] Avg episode reward: [(0, '22.805')]
[2023-02-25 17:16:39,468][15488] Updated weights for policy 0, policy_version 960 (0.0042)
[2023-02-25 17:16:43,467][09465] Fps is (10 sec: 2868.2, 60 sec: 3754.7, 300 sec: 3721.1). Total num frames: 3940352. Throughput: 0: 921.7. Samples: 984532. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:16:43,473][09465] Avg episode reward: [(0, '22.607')]
[2023-02-25 17:16:48,467][09465] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3748.9). Total num frames: 3964928. Throughput: 0: 944.4. Samples: 989942. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:16:48,470][09465] Avg episode reward: [(0, '22.773')]
[2023-02-25 17:16:50,114][15488] Updated weights for policy 0, policy_version 970 (0.0029)
[2023-02-25 17:16:53,467][09465] Fps is (10 sec: 4505.6, 60 sec: 3754.7, 300 sec: 3776.7). Total num frames: 3985408. Throughput: 0: 951.2. Samples: 996598. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-25 17:16:53,472][09465] Avg episode reward: [(0, '21.686')]
[2023-02-25 17:16:58,468][09465] Fps is (10 sec: 3686.4, 60 sec: 3755.0, 300 sec: 3748.9). Total num frames: 4001792. Throughput: 0: 930.3. Samples: 999164. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-25 17:16:58,473][09465] Avg episode reward: [(0, '21.724')]
[2023-02-25 17:16:59,601][15469] Stopping Batcher_0...
[2023-02-25 17:16:59,603][15469] Loop batcher_evt_loop terminating...
[2023-02-25 17:16:59,603][09465] Component Batcher_0 stopped!
[2023-02-25 17:16:59,610][15469] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
[2023-02-25 17:16:59,690][09465] Component RolloutWorker_w3 stopped!
[2023-02-25 17:16:59,697][15491] Stopping RolloutWorker_w3...
[2023-02-25 17:16:59,697][15491] Loop rollout_proc3_evt_loop terminating...
[2023-02-25 17:16:59,716][09465] Component RolloutWorker_w5 stopped!
[2023-02-25 17:16:59,713][15493] Stopping RolloutWorker_w5...
[2023-02-25 17:16:59,726][15488] Weights refcount: 2 0
[2023-02-25 17:16:59,728][15493] Loop rollout_proc5_evt_loop terminating...
[2023-02-25 17:16:59,747][15489] Stopping RolloutWorker_w1...
[2023-02-25 17:16:59,747][09465] Component InferenceWorker_p0-w0 stopped!
[2023-02-25 17:16:59,748][15495] Stopping RolloutWorker_w7...
[2023-02-25 17:16:59,749][15488] Stopping InferenceWorker_p0-w0...
[2023-02-25 17:16:59,751][15488] Loop inference_proc0-0_evt_loop terminating...
[2023-02-25 17:16:59,760][09465] Component RolloutWorker_w1 stopped!
[2023-02-25 17:16:59,764][15495] Loop rollout_proc7_evt_loop terminating...
[2023-02-25 17:16:59,762][15489] Loop rollout_proc1_evt_loop terminating...
[2023-02-25 17:16:59,763][09465] Component RolloutWorker_w7 stopped!
[2023-02-25 17:16:59,788][09465] Component RolloutWorker_w0 stopped!
[2023-02-25 17:16:59,799][15487] Stopping RolloutWorker_w0...
[2023-02-25 17:16:59,799][15487] Loop rollout_proc0_evt_loop terminating...
[2023-02-25 17:16:59,821][15469] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000831_3403776.pth
[2023-02-25 17:16:59,836][15469] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
[2023-02-25 17:16:59,837][09465] Component RolloutWorker_w2 stopped!
[2023-02-25 17:16:59,843][15490] Stopping RolloutWorker_w2...
[2023-02-25 17:16:59,844][15490] Loop rollout_proc2_evt_loop terminating...
[2023-02-25 17:16:59,857][15492] Stopping RolloutWorker_w4...
[2023-02-25 17:16:59,858][15492] Loop rollout_proc4_evt_loop terminating...
[2023-02-25 17:16:59,856][09465] Component RolloutWorker_w4 stopped!
[2023-02-25 17:16:59,871][09465] Component RolloutWorker_w6 stopped!
[2023-02-25 17:16:59,874][15494] Stopping RolloutWorker_w6...
[2023-02-25 17:16:59,874][15494] Loop rollout_proc6_evt_loop terminating...
[2023-02-25 17:17:00,166][09465] Component LearnerWorker_p0 stopped!
[2023-02-25 17:17:00,169][09465] Waiting for process learner_proc0 to stop...
[2023-02-25 17:17:00,171][15469] Stopping LearnerWorker_p0...
[2023-02-25 17:17:00,172][15469] Loop learner_proc0_evt_loop terminating...
[2023-02-25 17:17:02,401][09465] Waiting for process inference_proc0-0 to join...
[2023-02-25 17:17:03,180][09465] Waiting for process rollout_proc0 to join...
[2023-02-25 17:17:03,842][09465] Waiting for process rollout_proc1 to join...
[2023-02-25 17:17:03,845][09465] Waiting for process rollout_proc2 to join...
[2023-02-25 17:17:03,846][09465] Waiting for process rollout_proc3 to join...
[2023-02-25 17:17:03,848][09465] Waiting for process rollout_proc4 to join...
[2023-02-25 17:17:03,857][09465] Waiting for process rollout_proc5 to join...
[2023-02-25 17:17:03,858][09465] Waiting for process rollout_proc6 to join...
[2023-02-25 17:17:03,859][09465] Waiting for process rollout_proc7 to join...
[2023-02-25 17:17:03,860][09465] Batcher 0 profile tree view:
batching: 26.4756, releasing_batches: 0.0250
[2023-02-25 17:17:03,866][09465] InferenceWorker_p0-w0 profile tree view:
wait_policy: 0.0101
  wait_policy_total: 534.2617
update_model: 7.4674
  weight_update: 0.0032
one_step: 0.0088
  handle_policy_step: 508.4441
    deserialize: 14.7466, stack: 2.8181, obs_to_device_normalize: 114.0070, forward: 241.3566, send_messages: 26.3637
    prepare_outputs: 83.3078
      to_cpu: 52.3688
[2023-02-25 17:17:03,868][09465] Learner 0 profile tree view:
misc: 0.0066, prepare_batch: 17.4787
train: 76.3863
  epoch_init: 0.0056, minibatch_init: 0.0098, losses_postprocess: 0.4878, kl_divergence: 0.6374, after_optimizer: 33.0138
  calculate_losses: 27.2178
    losses_init: 0.0035, forward_head: 1.6556, bptt_initial: 17.9599, tail: 1.0816, advantages_returns: 0.3520, losses: 3.6775
    bptt: 2.1856
      bptt_forward_core: 2.1087
  update: 14.4016
    clip: 1.4061
[2023-02-25 17:17:03,869][09465] RolloutWorker_w0 profile tree view:
wait_for_trajectories: 0.3915, enqueue_policy_requests: 142.4984, env_step: 825.7155, overhead: 20.7273, complete_rollouts: 6.7821
save_policy_outputs: 20.0824
  split_output_tensors: 9.7964
[2023-02-25 17:17:03,871][09465] RolloutWorker_w7 profile tree view:
wait_for_trajectories: 0.3463, enqueue_policy_requests: 146.0947, env_step: 822.4486, overhead: 19.9156, complete_rollouts: 7.4870
save_policy_outputs: 18.9318
  split_output_tensors: 9.1657
[2023-02-25 17:17:03,877][09465] Loop Runner_EvtLoop terminating...
[2023-02-25 17:17:03,878][09465] Runner profile tree view:
main_loop: 1121.0972
[2023-02-25 17:17:03,880][09465] Collected {0: 4005888}, FPS: 3573.2
[2023-02-25 17:17:31,555][09465] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json
[2023-02-25 17:17:31,559][09465] Overriding arg 'num_workers' with value 1 passed from command line
[2023-02-25 17:17:31,561][09465] Adding new argument 'no_render'=True that is not in the saved config file!
[2023-02-25 17:17:31,564][09465] Adding new argument 'save_video'=True that is not in the saved config file!
[2023-02-25 17:17:31,566][09465] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
[2023-02-25 17:17:31,568][09465] Adding new argument 'video_name'=None that is not in the saved config file!
[2023-02-25 17:17:31,570][09465] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file!
[2023-02-25 17:17:31,571][09465] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
[2023-02-25 17:17:31,572][09465] Adding new argument 'push_to_hub'=False that is not in the saved config file!
[2023-02-25 17:17:31,574][09465] Adding new argument 'hf_repository'=None that is not in the saved config file!
[2023-02-25 17:17:31,575][09465] Adding new argument 'policy_index'=0 that is not in the saved config file!
[2023-02-25 17:17:31,576][09465] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
[2023-02-25 17:17:31,578][09465] Adding new argument 'train_script'=None that is not in the saved config file!
[2023-02-25 17:17:31,579][09465] Adding new argument 'enjoy_script'=None that is not in the saved config file!
[2023-02-25 17:17:31,581][09465] Using frameskip 1 and render_action_repeat=4 for evaluation
[2023-02-25 17:17:31,611][09465] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-25 17:17:31,615][09465] RunningMeanStd input shape: (3, 72, 128)
[2023-02-25 17:17:31,619][09465] RunningMeanStd input shape: (1,)
[2023-02-25 17:17:31,640][09465] ConvEncoder: input_channels=3
[2023-02-25 17:17:32,314][09465] Conv encoder output size: 512
[2023-02-25 17:17:32,316][09465] Policy head output size: 512
[2023-02-25 17:17:34,787][09465] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
[2023-02-25 17:17:36,130][09465] Num frames 100...
[2023-02-25 17:17:36,246][09465] Num frames 200...
[2023-02-25 17:17:36,371][09465] Num frames 300...
[2023-02-25 17:17:36,492][09465] Avg episode rewards: #0: 4.520, true rewards: #0: 3.520
[2023-02-25 17:17:36,494][09465] Avg episode reward: 4.520, avg true_objective: 3.520
[2023-02-25 17:17:36,552][09465] Num frames 400...
[2023-02-25 17:17:36,670][09465] Num frames 500...
[2023-02-25 17:17:36,795][09465] Num frames 600...
[2023-02-25 17:17:36,907][09465] Num frames 700...
[2023-02-25 17:17:37,027][09465] Num frames 800...
[2023-02-25 17:17:37,144][09465] Num frames 900...
[2023-02-25 17:17:37,254][09465] Num frames 1000...
[2023-02-25 17:17:37,366][09465] Num frames 1100...
[2023-02-25 17:17:37,492][09465] Num frames 1200...
[2023-02-25 17:17:37,621][09465] Avg episode rewards: #0: 10.825, true rewards: #0: 6.325
[2023-02-25 17:17:37,622][09465] Avg episode reward: 10.825, avg true_objective: 6.325
[2023-02-25 17:17:37,669][09465] Num frames 1300...
[2023-02-25 17:17:37,796][09465] Num frames 1400...
[2023-02-25 17:17:37,915][09465] Avg episode rewards: #0: 7.830, true rewards: #0: 4.830
[2023-02-25 17:17:37,916][09465] Avg episode reward: 7.830, avg true_objective: 4.830
[2023-02-25 17:17:37,978][09465] Num frames 1500...
[2023-02-25 17:17:38,098][09465] Num frames 1600...
[2023-02-25 17:17:38,234][09465] Num frames 1700...
[2023-02-25 17:17:38,348][09465] Num frames 1800...
[2023-02-25 17:17:38,466][09465] Num frames 1900...
[2023-02-25 17:17:38,590][09465] Num frames 2000...
[2023-02-25 17:17:38,713][09465] Num frames 2100...
[2023-02-25 17:17:38,902][09465] Num frames 2200...
[2023-02-25 17:17:39,075][09465] Num frames 2300...
[2023-02-25 17:17:39,237][09465] Num frames 2400...
[2023-02-25 17:17:39,403][09465] Num frames 2500...
[2023-02-25 17:17:39,565][09465] Num frames 2600...
[2023-02-25 17:17:39,785][09465] Avg episode rewards: #0: 13.243, true rewards: #0: 6.742
[2023-02-25 17:17:39,788][09465] Avg episode reward: 13.243, avg true_objective: 6.742
[2023-02-25 17:17:39,796][09465] Num frames 2700...
[2023-02-25 17:17:39,971][09465] Num frames 2800...
[2023-02-25 17:17:40,130][09465] Num frames 2900...
[2023-02-25 17:17:40,288][09465] Num frames 3000...
[2023-02-25 17:17:40,452][09465] Num frames 3100...
[2023-02-25 17:17:40,615][09465] Num frames 3200...
[2023-02-25 17:17:40,694][09465] Avg episode rewards: #0: 12.218, true rewards: #0: 6.418
[2023-02-25 17:17:40,697][09465] Avg episode reward: 12.218, avg true_objective: 6.418
[2023-02-25 17:17:40,853][09465] Num frames 3300...
[2023-02-25 17:17:41,030][09465] Num frames 3400...
[2023-02-25 17:17:41,192][09465] Num frames 3500...
[2023-02-25 17:17:41,355][09465] Num frames 3600...
[2023-02-25 17:17:41,527][09465] Num frames 3700...
[2023-02-25 17:17:41,697][09465] Num frames 3800...
[2023-02-25 17:17:41,867][09465] Num frames 3900...
[2023-02-25 17:17:42,037][09465] Num frames 4000...
[2023-02-25 17:17:42,209][09465] Num frames 4100...
[2023-02-25 17:17:42,375][09465] Num frames 4200...
[2023-02-25 17:17:42,518][09465] Num frames 4300...
[2023-02-25 17:17:42,638][09465] Num frames 4400...
[2023-02-25 17:17:42,755][09465] Num frames 4500...
[2023-02-25 17:17:42,888][09465] Num frames 4600...
[2023-02-25 17:17:43,024][09465] Num frames 4700...
[2023-02-25 17:17:43,140][09465] Num frames 4800...
[2023-02-25 17:17:43,253][09465] Num frames 4900...
[2023-02-25 17:17:43,319][09465] Avg episode rewards: #0: 17.013, true rewards: #0: 8.180
[2023-02-25 17:17:43,320][09465] Avg episode reward: 17.013, avg true_objective: 8.180
[2023-02-25 17:17:43,432][09465] Num frames 5000...
[2023-02-25 17:17:43,548][09465] Num frames 5100...
[2023-02-25 17:17:43,682][09465] Num frames 5200...
[2023-02-25 17:17:43,809][09465] Num frames 5300...
[2023-02-25 17:17:43,940][09465] Num frames 5400...
[2023-02-25 17:17:44,065][09465] Num frames 5500...
[2023-02-25 17:17:44,194][09465] Num frames 5600...
[2023-02-25 17:17:44,316][09465] Num frames 5700...
[2023-02-25 17:17:44,471][09465] Avg episode rewards: #0: 16.970, true rewards: #0: 8.256
[2023-02-25 17:17:44,473][09465] Avg episode reward: 16.970, avg true_objective: 8.256
[2023-02-25 17:17:44,501][09465] Num frames 5800...
[2023-02-25 17:17:44,631][09465] Num frames 5900...
[2023-02-25 17:17:44,750][09465] Num frames 6000...
[2023-02-25 17:17:44,872][09465] Num frames 6100...
[2023-02-25 17:17:44,996][09465] Num frames 6200...
[2023-02-25 17:17:45,134][09465] Num frames 6300...
[2023-02-25 17:17:45,248][09465] Num frames 6400...
[2023-02-25 17:17:45,371][09465] Num frames 6500...
[2023-02-25 17:17:45,514][09465] Avg episode rewards: #0: 16.974, true rewards: #0: 8.224
[2023-02-25 17:17:45,516][09465] Avg episode reward: 16.974, avg true_objective: 8.224
[2023-02-25 17:17:45,544][09465] Num frames 6600...
[2023-02-25 17:17:45,659][09465] Num frames 6700...
[2023-02-25 17:17:45,782][09465] Num frames 6800...
[2023-02-25 17:17:45,897][09465] Num frames 6900...
[2023-02-25 17:17:46,026][09465] Num frames 7000...
[2023-02-25 17:17:46,147][09465] Num frames 7100...
[2023-02-25 17:17:46,261][09465] Num frames 7200...
[2023-02-25 17:17:46,377][09465] Num frames 7300...
[2023-02-25 17:17:46,490][09465] Num frames 7400...
[2023-02-25 17:17:46,610][09465] Num frames 7500...
[2023-02-25 17:17:46,733][09465] Num frames 7600...
[2023-02-25 17:17:46,858][09465] Num frames 7700...
[2023-02-25 17:17:46,975][09465] Num frames 7800...
[2023-02-25 17:17:47,079][09465] Avg episode rewards: #0: 18.147, true rewards: #0: 8.702
[2023-02-25 17:17:47,081][09465] Avg episode reward: 18.147, avg true_objective: 8.702
[2023-02-25 17:17:47,172][09465] Num frames 7900...
[2023-02-25 17:17:47,288][09465] Num frames 8000...
[2023-02-25 17:17:47,401][09465] Num frames 8100...
[2023-02-25 17:17:47,515][09465] Num frames 8200...
[2023-02-25 17:17:47,633][09465] Num frames 8300...
[2023-02-25 17:17:47,747][09465] Num frames 8400...
[2023-02-25 17:17:47,868][09465] Num frames 8500...
[2023-02-25 17:17:48,005][09465] Avg episode rewards: #0: 17.668, true rewards: #0: 8.568
[2023-02-25 17:17:48,008][09465] Avg episode reward: 17.668, avg true_objective: 8.568
[2023-02-25 17:18:40,246][09465] Replay video saved to /content/train_dir/default_experiment/replay.mp4!
[2023-02-25 17:19:49,771][09465] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json
[2023-02-25 17:19:49,773][09465] Overriding arg 'num_workers' with value 1 passed from command line
[2023-02-25 17:19:49,774][09465] Adding new argument 'no_render'=True that is not in the saved config file!
[2023-02-25 17:19:49,776][09465] Adding new argument 'save_video'=True that is not in the saved config file!
[2023-02-25 17:19:49,783][09465] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
[2023-02-25 17:19:49,785][09465] Adding new argument 'video_name'=None that is not in the saved config file!
[2023-02-25 17:19:49,788][09465] Adding new argument 'max_num_frames'=100000 that is not in the saved config file!
[2023-02-25 17:19:49,789][09465] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
[2023-02-25 17:19:49,790][09465] Adding new argument 'push_to_hub'=True that is not in the saved config file!
[2023-02-25 17:19:49,791][09465] Adding new argument 'hf_repository'='morganjeffries/rl_course_vizdoom_health_gathering_supreme' that is not in the saved config file!
[2023-02-25 17:19:49,793][09465] Adding new argument 'policy_index'=0 that is not in the saved config file!
[2023-02-25 17:19:49,794][09465] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
[2023-02-25 17:19:49,795][09465] Adding new argument 'train_script'=None that is not in the saved config file!
[2023-02-25 17:19:49,796][09465] Adding new argument 'enjoy_script'=None that is not in the saved config file!
[2023-02-25 17:19:49,797][09465] Using frameskip 1 and render_action_repeat=4 for evaluation
[2023-02-25 17:19:49,825][09465] RunningMeanStd input shape: (3, 72, 128)
[2023-02-25 17:19:49,828][09465] RunningMeanStd input shape: (1,)
[2023-02-25 17:19:49,843][09465] ConvEncoder: input_channels=3
[2023-02-25 17:19:49,881][09465] Conv encoder output size: 512
[2023-02-25 17:19:49,882][09465] Policy head output size: 512
[2023-02-25 17:19:49,903][09465] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
[2023-02-25 17:19:50,381][09465] Num frames 100...
[2023-02-25 17:19:50,503][09465] Num frames 200...
[2023-02-25 17:19:50,628][09465] Num frames 300...
[2023-02-25 17:19:50,751][09465] Num frames 400...
[2023-02-25 17:19:50,902][09465] Num frames 500...
[2023-02-25 17:19:51,080][09465] Num frames 600...
[2023-02-25 17:19:51,252][09465] Num frames 700...
[2023-02-25 17:19:51,428][09465] Num frames 800...
[2023-02-25 17:19:51,589][09465] Num frames 900...
[2023-02-25 17:19:51,755][09465] Num frames 1000...
[2023-02-25 17:19:51,940][09465] Num frames 1100...
[2023-02-25 17:19:52,107][09465] Num frames 1200...
[2023-02-25 17:19:52,193][09465] Avg episode rewards: #0: 28.160, true rewards: #0: 12.160
[2023-02-25 17:19:52,198][09465] Avg episode reward: 28.160, avg true_objective: 12.160
[2023-02-25 17:19:52,338][09465] Num frames 1300...
[2023-02-25 17:19:52,517][09465] Num frames 1400...
[2023-02-25 17:19:52,682][09465] Num frames 1500...
[2023-02-25 17:19:52,849][09465] Num frames 1600...
[2023-02-25 17:19:53,017][09465] Num frames 1700...
[2023-02-25 17:19:53,176][09465] Num frames 1800...
[2023-02-25 17:19:53,335][09465] Num frames 1900...
[2023-02-25 17:19:53,496][09465] Num frames 2000...
[2023-02-25 17:19:53,660][09465] Num frames 2100...
[2023-02-25 17:19:53,822][09465] Num frames 2200...
[2023-02-25 17:19:53,984][09465] Num frames 2300...
[2023-02-25 17:19:54,302][09465] Num frames 2400...
[2023-02-25 17:19:54,706][09465] Avg episode rewards: #0: 26.980, true rewards: #0: 12.480
[2023-02-25 17:19:54,712][09465] Avg episode reward: 26.980, avg true_objective: 12.480
[2023-02-25 17:19:54,734][09465] Num frames 2500...
[2023-02-25 17:19:55,007][09465] Num frames 2600...
[2023-02-25 17:19:55,242][09465] Num frames 2700...
[2023-02-25 17:19:55,488][09465] Num frames 2800...
[2023-02-25 17:19:55,642][09465] Num frames 2900...
[2023-02-25 17:19:55,761][09465] Num frames 3000...
[2023-02-25 17:19:55,878][09465] Num frames 3100...
[2023-02-25 17:19:55,994][09465] Num frames 3200...
[2023-02-25 17:19:56,116][09465] Num frames 3300...
[2023-02-25 17:19:56,241][09465] Num frames 3400...
[2023-02-25 17:19:56,394][09465] Avg episode rewards: #0: 24.294, true rewards: #0: 11.627
[2023-02-25 17:19:56,396][09465] Avg episode reward: 24.294, avg true_objective: 11.627
[2023-02-25 17:19:56,417][09465] Num frames 3500...
[2023-02-25 17:19:56,535][09465] Num frames 3600...
[2023-02-25 17:19:56,660][09465] Num frames 3700...
[2023-02-25 17:19:56,775][09465] Num frames 3800...
[2023-02-25 17:19:56,894][09465] Num frames 3900...
[2023-02-25 17:19:56,956][09465] Avg episode rewards: #0: 19.760, true rewards: #0: 9.760
[2023-02-25 17:19:56,961][09465] Avg episode reward: 19.760, avg true_objective: 9.760
[2023-02-25 17:19:57,080][09465] Num frames 4000...
[2023-02-25 17:19:57,204][09465] Num frames 4100...
[2023-02-25 17:19:57,327][09465] Num frames 4200...
[2023-02-25 17:19:57,447][09465] Num frames 4300...
[2023-02-25 17:19:57,569][09465] Num frames 4400...
[2023-02-25 17:19:57,695][09465] Num frames 4500...
[2023-02-25 17:19:57,815][09465] Avg episode rewards: #0: 18.514, true rewards: #0: 9.114
[2023-02-25 17:19:57,817][09465] Avg episode reward: 18.514, avg true_objective: 9.114
[2023-02-25 17:19:57,871][09465] Num frames 4600...
[2023-02-25 17:19:58,002][09465] Num frames 4700...
[2023-02-25 17:19:58,124][09465] Num frames 4800...
[2023-02-25 17:19:58,241][09465] Num frames 4900...
[2023-02-25 17:19:58,360][09465] Num frames 5000...
[2023-02-25 17:19:58,487][09465] Num frames 5100...
[2023-02-25 17:19:58,584][09465] Avg episode rewards: #0: 17.055, true rewards: #0: 8.555
[2023-02-25 17:19:58,585][09465] Avg episode reward: 17.055, avg true_objective: 8.555
[2023-02-25 17:19:58,686][09465] Num frames 5200...
[2023-02-25 17:19:58,802][09465] Num frames 5300...
[2023-02-25 17:19:58,916][09465] Num frames 5400...
[2023-02-25 17:19:59,034][09465] Num frames 5500...
[2023-02-25 17:19:59,154][09465] Num frames 5600...
[2023-02-25 17:19:59,279][09465] Num frames 5700...
[2023-02-25 17:19:59,395][09465] Num frames 5800...
[2023-02-25 17:19:59,512][09465] Num frames 5900...
[2023-02-25 17:19:59,628][09465] Num frames 6000...
[2023-02-25 17:19:59,758][09465] Num frames 6100...
[2023-02-25 17:19:59,876][09465] Num frames 6200...
[2023-02-25 17:19:59,991][09465] Num frames 6300...
[2023-02-25 17:20:00,115][09465] Num frames 6400...
[2023-02-25 17:20:00,242][09465] Num frames 6500...
[2023-02-25 17:20:00,361][09465] Num frames 6600...
[2023-02-25 17:20:00,484][09465] Num frames 6700...
[2023-02-25 17:20:00,601][09465] Num frames 6800...
[2023-02-25 17:20:00,726][09465] Num frames 6900...
[2023-02-25 17:20:00,901][09465] Avg episode rewards: #0: 21.423, true rewards: #0: 9.994
[2023-02-25 17:20:00,903][09465] Avg episode reward: 21.423, avg true_objective: 9.994
[2023-02-25 17:20:00,911][09465] Num frames 7000...
[2023-02-25 17:20:01,022][09465] Num frames 7100...
[2023-02-25 17:20:01,139][09465] Num frames 7200...
[2023-02-25 17:20:01,260][09465] Num frames 7300...
[2023-02-25 17:20:01,380][09465] Num frames 7400...
[2023-02-25 17:20:01,500][09465] Num frames 7500...
[2023-02-25 17:20:01,615][09465] Num frames 7600...
[2023-02-25 17:20:01,748][09465] Num frames 7700...
[2023-02-25 17:20:01,877][09465] Num frames 7800...
[2023-02-25 17:20:02,007][09465] Num frames 7900...
[2023-02-25 17:20:02,147][09465] Num frames 8000...
[2023-02-25 17:20:02,264][09465] Num frames 8100...
[2023-02-25 17:20:02,389][09465] Num frames 8200...
[2023-02-25 17:20:02,513][09465] Num frames 8300...
[2023-02-25 17:20:02,635][09465] Num frames 8400...
[2023-02-25 17:20:02,756][09465] Num frames 8500...
[2023-02-25 17:20:02,880][09465] Num frames 8600...
[2023-02-25 17:20:02,994][09465] Num frames 8700...
[2023-02-25 17:20:03,111][09465] Num frames 8800...
[2023-02-25 17:20:03,237][09465] Num frames 8900...
[2023-02-25 17:20:03,355][09465] Num frames 9000...
[2023-02-25 17:20:03,468][09465] Avg episode rewards: #0: 25.680, true rewards: #0: 11.305
[2023-02-25 17:20:03,469][09465] Avg episode reward: 25.680, avg true_objective: 11.305
[2023-02-25 17:20:03,536][09465] Num frames 9100...
[2023-02-25 17:20:03,655][09465] Num frames 9200...
[2023-02-25 17:20:03,776][09465] Num frames 9300...
[2023-02-25 17:20:03,894][09465] Num frames 9400...
[2023-02-25 17:20:04,016][09465] Num frames 9500...
[2023-02-25 17:20:04,136][09465] Num frames 9600...
[2023-02-25 17:20:04,220][09465] Avg episode rewards: #0: 23.911, true rewards: #0: 10.689
[2023-02-25 17:20:04,223][09465] Avg episode reward: 23.911, avg true_objective: 10.689
[2023-02-25 17:20:04,316][09465] Num frames 9700...
[2023-02-25 17:20:04,436][09465] Num frames 9800...
[2023-02-25 17:20:04,561][09465] Num frames 9900...
[2023-02-25 17:20:04,679][09465] Num frames 10000...
[2023-02-25 17:20:04,805][09465] Num frames 10100...
[2023-02-25 17:20:04,964][09465] Num frames 10200...
[2023-02-25 17:20:05,133][09465] Num frames 10300...
[2023-02-25 17:20:05,307][09465] Num frames 10400...
[2023-02-25 17:20:05,471][09465] Num frames 10500...
[2023-02-25 17:20:05,637][09465] Num frames 10600...
[2023-02-25 17:20:05,813][09465] Num frames 10700...
[2023-02-25 17:20:05,981][09465] Num frames 10800...
[2023-02-25 17:20:06,149][09465] Num frames 10900...
[2023-02-25 17:20:06,309][09465] Num frames 11000...
[2023-02-25 17:20:06,531][09465] Avg episode rewards: #0: 25.399, true rewards: #0: 11.099
[2023-02-25 17:20:06,537][09465] Avg episode reward: 25.399, avg true_objective: 11.099
[2023-02-25 17:20:06,542][09465] Num frames 11100...
[2023-02-25 17:21:16,250][09465] Replay video saved to /content/train_dir/default_experiment/replay.mp4!