[2023-02-23 09:08:23,651][00405] Saving configuration to /content/train_dir/default_experiment/config.json...
[2023-02-23 09:08:23,653][00405] Rollout worker 0 uses device cpu
[2023-02-23 09:08:23,654][00405] Rollout worker 1 uses device cpu
[2023-02-23 09:08:23,656][00405] Rollout worker 2 uses device cpu
[2023-02-23 09:08:23,657][00405] Rollout worker 3 uses device cpu
[2023-02-23 09:08:23,658][00405] Rollout worker 4 uses device cpu
[2023-02-23 09:08:23,659][00405] Rollout worker 5 uses device cpu
[2023-02-23 09:08:23,660][00405] Rollout worker 6 uses device cpu
[2023-02-23 09:08:23,662][00405] Rollout worker 7 uses device cpu
[2023-02-23 09:08:23,860][00405] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-02-23 09:08:23,863][00405] InferenceWorker_p0-w0: min num requests: 2
[2023-02-23 09:08:23,892][00405] Starting all processes...
[2023-02-23 09:08:23,893][00405] Starting process learner_proc0
[2023-02-23 09:08:23,946][00405] Starting all processes...
[2023-02-23 09:08:23,955][00405] Starting process inference_proc0-0
[2023-02-23 09:08:23,956][00405] Starting process rollout_proc0
[2023-02-23 09:08:23,956][00405] Starting process rollout_proc1
[2023-02-23 09:08:23,956][00405] Starting process rollout_proc2
[2023-02-23 09:08:23,956][00405] Starting process rollout_proc3
[2023-02-23 09:08:23,956][00405] Starting process rollout_proc4
[2023-02-23 09:08:23,956][00405] Starting process rollout_proc5
[2023-02-23 09:08:23,956][00405] Starting process rollout_proc6
[2023-02-23 09:08:23,956][00405] Starting process rollout_proc7
[2023-02-23 09:08:33,302][26332] Worker 7 uses CPU cores [1]
[2023-02-23 09:08:33,433][26325] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-02-23 09:08:33,433][26325] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
[2023-02-23 09:08:33,489][26306] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-02-23 09:08:33,491][26306] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
[2023-02-23 09:08:33,583][26326] Worker 0 uses CPU cores [0]
[2023-02-23 09:08:33,663][26331] Worker 6 uses CPU cores [0]
[2023-02-23 09:08:33,683][26329] Worker 3 uses CPU cores [1]
[2023-02-23 09:08:33,736][26324] Worker 1 uses CPU cores [1]
[2023-02-23 09:08:33,735][26327] Worker 2 uses CPU cores [0]
[2023-02-23 09:08:33,853][26330] Worker 5 uses CPU cores [1]
[2023-02-23 09:08:34,060][26328] Worker 4 uses CPU cores [0]
[2023-02-23 09:08:34,398][26306] Num visible devices: 1
[2023-02-23 09:08:34,399][26306] Starting seed is not provided
[2023-02-23 09:08:34,399][26306] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-02-23 09:08:34,399][26306] Initializing actor-critic model on device cuda:0
[2023-02-23 09:08:34,400][26306] RunningMeanStd input shape: (3, 72, 128)
[2023-02-23 09:08:34,402][26306] RunningMeanStd input shape: (1,)
[2023-02-23 09:08:34,400][26325] Num visible devices: 1
[2023-02-23 09:08:34,421][26306] ConvEncoder: input_channels=3
[2023-02-23 09:08:34,775][26306] Conv encoder output size: 512
[2023-02-23 09:08:34,775][26306] Policy head output size: 512
[2023-02-23 09:08:34,836][26306] Created Actor Critic model with architecture:
[2023-02-23 09:08:34,837][26306] 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)
  )
)
[2023-02-23 09:08:42,281][26306] Using optimizer <class 'torch.optim.adam.Adam'>
[2023-02-23 09:08:42,282][26306] No checkpoints found
[2023-02-23 09:08:42,283][26306] Did not load from checkpoint, starting from scratch!
[2023-02-23 09:08:42,283][26306] Initialized policy 0 weights for model version 0
[2023-02-23 09:08:42,289][26306] LearnerWorker_p0 finished initialization!
[2023-02-23 09:08:42,289][26306] Using GPUs [0] for process 0 (actually maps to GPUs [0])
[2023-02-23 09:08:42,499][26325] RunningMeanStd input shape: (3, 72, 128)
[2023-02-23 09:08:42,500][26325] RunningMeanStd input shape: (1,)
[2023-02-23 09:08:42,512][26325] ConvEncoder: input_channels=3
[2023-02-23 09:08:42,608][26325] Conv encoder output size: 512
[2023-02-23 09:08:42,608][26325] Policy head output size: 512
[2023-02-23 09:08:43,853][00405] Heartbeat connected on Batcher_0
[2023-02-23 09:08:43,856][00405] Heartbeat connected on LearnerWorker_p0
[2023-02-23 09:08:43,872][00405] Heartbeat connected on RolloutWorker_w0
[2023-02-23 09:08:43,877][00405] Heartbeat connected on RolloutWorker_w1
[2023-02-23 09:08:43,880][00405] Heartbeat connected on RolloutWorker_w2
[2023-02-23 09:08:43,882][00405] Heartbeat connected on RolloutWorker_w3
[2023-02-23 09:08:43,887][00405] Heartbeat connected on RolloutWorker_w4
[2023-02-23 09:08:43,890][00405] Heartbeat connected on RolloutWorker_w5
[2023-02-23 09:08:43,894][00405] Heartbeat connected on RolloutWorker_w6
[2023-02-23 09:08:43,897][00405] Heartbeat connected on RolloutWorker_w7
[2023-02-23 09:08:43,988][00405] 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)
[2023-02-23 09:08:44,888][00405] Inference worker 0-0 is ready!
[2023-02-23 09:08:44,890][00405] All inference workers are ready! Signal rollout workers to start!
[2023-02-23 09:08:44,892][00405] Heartbeat connected on InferenceWorker_p0-w0
[2023-02-23 09:08:44,994][26326] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-23 09:08:45,017][26328] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-23 09:08:45,020][26327] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-23 09:08:45,036][26331] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-23 09:08:45,049][26329] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-23 09:08:45,050][26332] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-23 09:08:45,063][26324] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-23 09:08:45,068][26330] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-23 09:08:46,190][26329] Decorrelating experience for 0 frames...
[2023-02-23 09:08:46,191][26330] Decorrelating experience for 0 frames...
[2023-02-23 09:08:46,189][26327] Decorrelating experience for 0 frames...
[2023-02-23 09:08:46,187][26326] Decorrelating experience for 0 frames...
[2023-02-23 09:08:46,192][26332] Decorrelating experience for 0 frames...
[2023-02-23 09:08:46,190][26328] Decorrelating experience for 0 frames...
[2023-02-23 09:08:47,377][26326] Decorrelating experience for 32 frames...
[2023-02-23 09:08:47,395][26328] Decorrelating experience for 32 frames...
[2023-02-23 09:08:47,419][26327] Decorrelating experience for 32 frames...
[2023-02-23 09:08:47,593][26324] Decorrelating experience for 0 frames...
[2023-02-23 09:08:47,633][26332] Decorrelating experience for 32 frames...
[2023-02-23 09:08:47,635][26330] Decorrelating experience for 32 frames...
[2023-02-23 09:08:47,641][26329] Decorrelating experience for 32 frames...
[2023-02-23 09:08:48,991][00405] 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)
[2023-02-23 09:08:49,261][26326] Decorrelating experience for 64 frames...
[2023-02-23 09:08:49,274][26328] Decorrelating experience for 64 frames...
[2023-02-23 09:08:49,339][26327] Decorrelating experience for 64 frames...
[2023-02-23 09:08:49,572][26324] Decorrelating experience for 32 frames...
[2023-02-23 09:08:49,858][26330] Decorrelating experience for 64 frames...
[2023-02-23 09:08:49,869][26332] Decorrelating experience for 64 frames...
[2023-02-23 09:08:50,724][26329] Decorrelating experience for 64 frames...
[2023-02-23 09:08:51,006][26331] Decorrelating experience for 0 frames...
[2023-02-23 09:08:51,014][26326] Decorrelating experience for 96 frames...
[2023-02-23 09:08:51,033][26328] Decorrelating experience for 96 frames...
[2023-02-23 09:08:51,584][26332] Decorrelating experience for 96 frames...
[2023-02-23 09:08:52,041][26324] Decorrelating experience for 64 frames...
[2023-02-23 09:08:52,179][26330] Decorrelating experience for 96 frames...
[2023-02-23 09:08:52,634][26329] Decorrelating experience for 96 frames...
[2023-02-23 09:08:52,982][26327] Decorrelating experience for 96 frames...
[2023-02-23 09:08:53,010][26331] Decorrelating experience for 32 frames...
[2023-02-23 09:08:53,850][26331] Decorrelating experience for 64 frames...
[2023-02-23 09:08:53,952][26324] Decorrelating experience for 96 frames...
[2023-02-23 09:08:53,987][00405] 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)
[2023-02-23 09:08:54,551][26331] Decorrelating experience for 96 frames...
[2023-02-23 09:08:58,250][26306] Signal inference workers to stop experience collection...
[2023-02-23 09:08:58,256][26325] InferenceWorker_p0-w0: stopping experience collection
[2023-02-23 09:08:58,987][00405] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 82.7. Samples: 1240. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0)
[2023-02-23 09:08:58,989][00405] Avg episode reward: [(0, '1.580')]
[2023-02-23 09:09:00,782][26306] Signal inference workers to resume experience collection...
[2023-02-23 09:09:00,785][26325] InferenceWorker_p0-w0: resuming experience collection
[2023-02-23 09:09:03,987][00405] Fps is (10 sec: 1638.4, 60 sec: 819.2, 300 sec: 819.2). Total num frames: 16384. Throughput: 0: 189.7. Samples: 3794. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-23 09:09:03,994][00405] Avg episode reward: [(0, '3.261')]
[2023-02-23 09:09:08,989][00405] Fps is (10 sec: 3276.2, 60 sec: 1310.6, 300 sec: 1310.6). Total num frames: 32768. Throughput: 0: 365.7. Samples: 9144. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0)
[2023-02-23 09:09:08,997][00405] Avg episode reward: [(0, '3.738')]
[2023-02-23 09:09:10,959][26325] Updated weights for policy 0, policy_version 10 (0.0357)
[2023-02-23 09:09:13,987][00405] Fps is (10 sec: 3276.8, 60 sec: 1638.4, 300 sec: 1638.4). Total num frames: 49152. Throughput: 0: 379.1. Samples: 11374. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-23 09:09:13,990][00405] Avg episode reward: [(0, '4.304')]
[2023-02-23 09:09:18,988][00405] Fps is (10 sec: 3686.8, 60 sec: 1989.5, 300 sec: 1989.5). Total num frames: 69632. Throughput: 0: 486.8. Samples: 17038. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0)
[2023-02-23 09:09:18,993][00405] Avg episode reward: [(0, '4.531')]
[2023-02-23 09:09:21,223][26325] Updated weights for policy 0, policy_version 20 (0.0015)
[2023-02-23 09:09:23,990][00405] Fps is (10 sec: 4094.9, 60 sec: 2252.7, 300 sec: 2252.7). Total num frames: 90112. Throughput: 0: 600.7. Samples: 24030. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:09:23,997][00405] Avg episode reward: [(0, '4.617')]
[2023-02-23 09:09:28,989][00405] Fps is (10 sec: 4095.4, 60 sec: 2457.5, 300 sec: 2457.5). Total num frames: 110592. Throughput: 0: 591.0. Samples: 26596. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:09:28,992][00405] Avg episode reward: [(0, '4.488')]
[2023-02-23 09:09:29,004][26306] Saving new best policy, reward=4.488!
[2023-02-23 09:09:33,385][26325] Updated weights for policy 0, policy_version 30 (0.0013)
[2023-02-23 09:09:33,987][00405] Fps is (10 sec: 3277.7, 60 sec: 2457.6, 300 sec: 2457.6). Total num frames: 122880. Throughput: 0: 690.0. Samples: 31046. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:09:33,991][00405] Avg episode reward: [(0, '4.391')]
[2023-02-23 09:09:38,987][00405] Fps is (10 sec: 3277.5, 60 sec: 2606.5, 300 sec: 2606.5). Total num frames: 143360. Throughput: 0: 829.6. Samples: 37334. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:09:38,990][00405] Avg episode reward: [(0, '4.290')]
[2023-02-23 09:09:42,561][26325] Updated weights for policy 0, policy_version 40 (0.0017)
[2023-02-23 09:09:43,987][00405] Fps is (10 sec: 4505.6, 60 sec: 2798.9, 300 sec: 2798.9). Total num frames: 167936. Throughput: 0: 878.7. Samples: 40782. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:09:43,989][00405] Avg episode reward: [(0, '4.289')]
[2023-02-23 09:09:48,991][00405] Fps is (10 sec: 4094.5, 60 sec: 3072.0, 300 sec: 2835.5). Total num frames: 184320. Throughput: 0: 945.3. Samples: 46338. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:09:48,996][00405] Avg episode reward: [(0, '4.395')]
[2023-02-23 09:09:53,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3345.1, 300 sec: 2867.2). Total num frames: 200704. Throughput: 0: 924.4. Samples: 50738. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:09:53,990][00405] Avg episode reward: [(0, '4.389')]
[2023-02-23 09:09:55,062][26325] Updated weights for policy 0, policy_version 50 (0.0011)
[2023-02-23 09:09:58,987][00405] Fps is (10 sec: 3687.7, 60 sec: 3686.4, 300 sec: 2949.1). Total num frames: 221184. Throughput: 0: 945.3. Samples: 53912. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:09:58,989][00405] Avg episode reward: [(0, '4.467')]
[2023-02-23 09:10:03,688][26325] Updated weights for policy 0, policy_version 60 (0.0014)
[2023-02-23 09:10:03,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3822.9, 300 sec: 3072.0). Total num frames: 245760. Throughput: 0: 974.8. Samples: 60902. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:10:03,990][00405] Avg episode reward: [(0, '4.436')]
[2023-02-23 09:10:08,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3823.1, 300 sec: 3084.1). Total num frames: 262144. Throughput: 0: 933.0. Samples: 66014. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0)
[2023-02-23 09:10:08,993][00405] Avg episode reward: [(0, '4.410')]
[2023-02-23 09:10:13,988][00405] Fps is (10 sec: 2867.0, 60 sec: 3754.6, 300 sec: 3049.2). Total num frames: 274432. Throughput: 0: 923.5. Samples: 68150. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-23 09:10:13,998][00405] Avg episode reward: [(0, '4.405')]
[2023-02-23 09:10:16,321][26325] Updated weights for policy 0, policy_version 70 (0.0012)
[2023-02-23 09:10:18,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3823.0, 300 sec: 3147.5). Total num frames: 299008. Throughput: 0: 956.4. Samples: 74082. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:10:18,990][00405] Avg episode reward: [(0, '4.338')]
[2023-02-23 09:10:19,001][26306] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000073_299008.pth...
[2023-02-23 09:10:23,987][00405] Fps is (10 sec: 4505.9, 60 sec: 3823.1, 300 sec: 3194.9). Total num frames: 319488. Throughput: 0: 968.6. Samples: 80920. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:10:23,995][00405] Avg episode reward: [(0, '4.525')]
[2023-02-23 09:10:24,001][26306] Saving new best policy, reward=4.525!
[2023-02-23 09:10:25,697][26325] Updated weights for policy 0, policy_version 80 (0.0023)
[2023-02-23 09:10:28,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3754.8, 300 sec: 3198.8). Total num frames: 335872. Throughput: 0: 944.8. Samples: 83300. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-23 09:10:28,997][00405] Avg episode reward: [(0, '4.565')]
[2023-02-23 09:10:29,016][26306] Saving new best policy, reward=4.565!
[2023-02-23 09:10:33,987][00405] Fps is (10 sec: 2867.2, 60 sec: 3754.7, 300 sec: 3165.1). Total num frames: 348160. Throughput: 0: 916.5. Samples: 87578. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:10:33,993][00405] Avg episode reward: [(0, '4.314')]
[2023-02-23 09:10:37,885][26325] Updated weights for policy 0, policy_version 90 (0.0022)
[2023-02-23 09:10:38,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3241.2). Total num frames: 372736. Throughput: 0: 957.3. Samples: 93816. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:10:38,990][00405] Avg episode reward: [(0, '4.234')]
[2023-02-23 09:10:43,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3754.7, 300 sec: 3276.8). Total num frames: 393216. Throughput: 0: 963.5. Samples: 97268. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:10:43,990][00405] Avg episode reward: [(0, '4.596')]
[2023-02-23 09:10:44,000][26306] Saving new best policy, reward=4.596!
[2023-02-23 09:10:48,424][26325] Updated weights for policy 0, policy_version 100 (0.0032)
[2023-02-23 09:10:48,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3754.9, 300 sec: 3276.8). Total num frames: 409600. Throughput: 0: 926.4. Samples: 102592. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:10:48,991][00405] Avg episode reward: [(0, '4.608')]
[2023-02-23 09:10:49,002][26306] Saving new best policy, reward=4.608!
[2023-02-23 09:10:53,987][00405] Fps is (10 sec: 2867.2, 60 sec: 3686.4, 300 sec: 3245.3). Total num frames: 421888. Throughput: 0: 910.8. Samples: 107000. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
[2023-02-23 09:10:53,995][00405] Avg episode reward: [(0, '4.575')]
[2023-02-23 09:10:58,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3307.1). Total num frames: 446464. Throughput: 0: 936.5. Samples: 110294. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:10:58,990][00405] Avg episode reward: [(0, '4.627')]
[2023-02-23 09:10:59,003][26306] Saving new best policy, reward=4.627!
[2023-02-23 09:10:59,422][26325] Updated weights for policy 0, policy_version 110 (0.0034)
[2023-02-23 09:11:03,987][00405] Fps is (10 sec: 4915.2, 60 sec: 3754.7, 300 sec: 3364.6). Total num frames: 471040. Throughput: 0: 960.4. Samples: 117302. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:11:03,991][00405] Avg episode reward: [(0, '4.508')]
[2023-02-23 09:11:08,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3686.4, 300 sec: 3333.3). Total num frames: 483328. Throughput: 0: 918.2. Samples: 122240. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:11:08,990][00405] Avg episode reward: [(0, '4.405')]
[2023-02-23 09:11:10,545][26325] Updated weights for policy 0, policy_version 120 (0.0022)
[2023-02-23 09:11:13,987][00405] Fps is (10 sec: 2867.2, 60 sec: 3754.7, 300 sec: 3331.4). Total num frames: 499712. Throughput: 0: 913.5. Samples: 124408. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:11:13,994][00405] Avg episode reward: [(0, '4.469')]
[2023-02-23 09:11:18,988][00405] Fps is (10 sec: 4095.9, 60 sec: 3754.6, 300 sec: 3382.5). Total num frames: 524288. Throughput: 0: 955.4. Samples: 130572. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:11:18,990][00405] Avg episode reward: [(0, '4.735')]
[2023-02-23 09:11:18,997][26306] Saving new best policy, reward=4.735!
[2023-02-23 09:11:20,704][26325] Updated weights for policy 0, policy_version 130 (0.0031)
[2023-02-23 09:11:23,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3754.7, 300 sec: 3404.8). Total num frames: 544768. Throughput: 0: 966.5. Samples: 137308. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:11:23,990][00405] Avg episode reward: [(0, '4.763')]
[2023-02-23 09:11:23,992][26306] Saving new best policy, reward=4.763!
[2023-02-23 09:11:28,987][00405] Fps is (10 sec: 3686.5, 60 sec: 3754.7, 300 sec: 3400.9). Total num frames: 561152. Throughput: 0: 938.2. Samples: 139488. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:11:28,996][00405] Avg episode reward: [(0, '4.460')]
[2023-02-23 09:11:32,776][26325] Updated weights for policy 0, policy_version 140 (0.0026)
[2023-02-23 09:11:33,987][00405] Fps is (10 sec: 2867.2, 60 sec: 3754.7, 300 sec: 3373.2). Total num frames: 573440. Throughput: 0: 919.6. Samples: 143972. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:11:33,992][00405] Avg episode reward: [(0, '4.426')]
[2023-02-23 09:11:38,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3417.2). Total num frames: 598016. Throughput: 0: 965.9. Samples: 150464. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:11:38,993][00405] Avg episode reward: [(0, '4.358')]
[2023-02-23 09:11:42,118][26325] Updated weights for policy 0, policy_version 150 (0.0023)
[2023-02-23 09:11:43,987][00405] Fps is (10 sec: 4915.2, 60 sec: 3822.9, 300 sec: 3458.8). Total num frames: 622592. Throughput: 0: 970.5. Samples: 153966. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:11:43,995][00405] Avg episode reward: [(0, '4.589')]
[2023-02-23 09:11:48,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3431.8). Total num frames: 634880. Throughput: 0: 933.4. Samples: 159304. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:11:48,992][00405] Avg episode reward: [(0, '4.763')]
[2023-02-23 09:11:53,987][00405] Fps is (10 sec: 2867.2, 60 sec: 3822.9, 300 sec: 3427.7). Total num frames: 651264. Throughput: 0: 921.0. Samples: 163686. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:11:53,990][00405] Avg episode reward: [(0, '4.886')]
[2023-02-23 09:11:53,993][26306] Saving new best policy, reward=4.886!
[2023-02-23 09:11:54,496][26325] Updated weights for policy 0, policy_version 160 (0.0019)
[2023-02-23 09:11:58,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3444.8). Total num frames: 671744. Throughput: 0: 948.7. Samples: 167098. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:11:58,990][00405] Avg episode reward: [(0, '4.695')]
[2023-02-23 09:12:03,608][26325] Updated weights for policy 0, policy_version 170 (0.0013)
[2023-02-23 09:12:03,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3754.7, 300 sec: 3481.6). Total num frames: 696320. Throughput: 0: 965.6. Samples: 174022. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:12:03,990][00405] Avg episode reward: [(0, '4.289')]
[2023-02-23 09:12:08,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3476.6). Total num frames: 712704. Throughput: 0: 920.5. Samples: 178730. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:12:08,990][00405] Avg episode reward: [(0, '4.308')]
[2023-02-23 09:12:13,987][00405] Fps is (10 sec: 2867.2, 60 sec: 3754.7, 300 sec: 3452.3). Total num frames: 724992. Throughput: 0: 921.4. Samples: 180952. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:12:13,989][00405] Avg episode reward: [(0, '4.348')]
[2023-02-23 09:12:16,059][26325] Updated weights for policy 0, policy_version 180 (0.0032)
[2023-02-23 09:12:18,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3486.4). Total num frames: 749568. Throughput: 0: 956.6. Samples: 187020. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:12:18,990][00405] Avg episode reward: [(0, '4.600')]
[2023-02-23 09:12:18,998][26306] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000183_749568.pth...
[2023-02-23 09:12:23,990][00405] Fps is (10 sec: 4504.4, 60 sec: 3754.5, 300 sec: 3500.2). Total num frames: 770048. Throughput: 0: 966.4. Samples: 193954. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:12:23,993][00405] Avg episode reward: [(0, '4.923')]
[2023-02-23 09:12:23,995][26306] Saving new best policy, reward=4.923!
[2023-02-23 09:12:25,812][26325] Updated weights for policy 0, policy_version 190 (0.0014)
[2023-02-23 09:12:28,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3495.3). Total num frames: 786432. Throughput: 0: 936.0. Samples: 196088. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:12:28,991][00405] Avg episode reward: [(0, '4.716')]
[2023-02-23 09:12:33,987][00405] Fps is (10 sec: 3277.6, 60 sec: 3822.9, 300 sec: 3490.5). Total num frames: 802816. Throughput: 0: 915.0. Samples: 200478. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:12:33,990][00405] Avg episode reward: [(0, '4.474')]
[2023-02-23 09:12:37,415][26325] Updated weights for policy 0, policy_version 200 (0.0018)
[2023-02-23 09:12:38,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3503.4). Total num frames: 823296. Throughput: 0: 966.8. Samples: 207192. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:12:38,995][00405] Avg episode reward: [(0, '4.687')]
[2023-02-23 09:12:43,989][00405] Fps is (10 sec: 4504.9, 60 sec: 3754.6, 300 sec: 3532.8). Total num frames: 847872. Throughput: 0: 969.7. Samples: 210734. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:12:43,995][00405] Avg episode reward: [(0, '4.575')]
[2023-02-23 09:12:47,454][26325] Updated weights for policy 0, policy_version 210 (0.0035)
[2023-02-23 09:12:48,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3527.6). Total num frames: 864256. Throughput: 0: 932.4. Samples: 215978. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:12:48,995][00405] Avg episode reward: [(0, '4.402')]
[2023-02-23 09:12:53,987][00405] Fps is (10 sec: 2867.6, 60 sec: 3754.7, 300 sec: 3506.2). Total num frames: 876544. Throughput: 0: 925.4. Samples: 220372. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:12:53,996][00405] Avg episode reward: [(0, '4.490')]
[2023-02-23 09:12:58,742][26325] Updated weights for policy 0, policy_version 220 (0.0023)
[2023-02-23 09:12:58,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3533.8). Total num frames: 901120. Throughput: 0: 950.5. Samples: 223726. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:12:58,990][00405] Avg episode reward: [(0, '4.536')]
[2023-02-23 09:13:03,988][00405] Fps is (10 sec: 4505.3, 60 sec: 3754.6, 300 sec: 3544.6). Total num frames: 921600. Throughput: 0: 971.7. Samples: 230748. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:13:03,992][00405] Avg episode reward: [(0, '4.635')]
[2023-02-23 09:13:08,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3539.6). Total num frames: 937984. Throughput: 0: 927.0. Samples: 235668. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:13:08,991][00405] Avg episode reward: [(0, '4.766')]
[2023-02-23 09:13:09,530][26325] Updated weights for policy 0, policy_version 230 (0.0015)
[2023-02-23 09:13:13,989][00405] Fps is (10 sec: 3276.5, 60 sec: 3822.8, 300 sec: 3534.7). Total num frames: 954368. Throughput: 0: 928.5. Samples: 237874. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:13:13,992][00405] Avg episode reward: [(0, '4.697')]
[2023-02-23 09:13:18,988][00405] Fps is (10 sec: 3686.2, 60 sec: 3754.6, 300 sec: 3544.9). Total num frames: 974848. Throughput: 0: 966.4. Samples: 243968. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:13:18,996][00405] Avg episode reward: [(0, '4.413')]
[2023-02-23 09:13:20,001][26325] Updated weights for policy 0, policy_version 240 (0.0024)
[2023-02-23 09:13:23,987][00405] Fps is (10 sec: 4506.3, 60 sec: 3823.1, 300 sec: 3569.4). Total num frames: 999424. Throughput: 0: 971.8. Samples: 250924. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:13:23,994][00405] Avg episode reward: [(0, '4.593')]
[2023-02-23 09:13:28,989][00405] Fps is (10 sec: 4095.6, 60 sec: 3822.8, 300 sec: 3564.2). Total num frames: 1015808. Throughput: 0: 942.8. Samples: 253158. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:13:28,995][00405] Avg episode reward: [(0, '4.668')]
[2023-02-23 09:13:31,501][26325] Updated weights for policy 0, policy_version 250 (0.0013)
[2023-02-23 09:13:33,988][00405] Fps is (10 sec: 2867.1, 60 sec: 3754.6, 300 sec: 3545.2). Total num frames: 1028096. Throughput: 0: 923.1. Samples: 257518. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:13:33,994][00405] Avg episode reward: [(0, '4.848')]
[2023-02-23 09:13:38,987][00405] Fps is (10 sec: 3687.0, 60 sec: 3822.9, 300 sec: 3568.4). Total num frames: 1052672. Throughput: 0: 968.2. Samples: 263942. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:13:38,995][00405] Avg episode reward: [(0, '4.684')]
[2023-02-23 09:13:41,405][26325] Updated weights for policy 0, policy_version 260 (0.0014)
[2023-02-23 09:13:43,987][00405] Fps is (10 sec: 4915.4, 60 sec: 3823.0, 300 sec: 3651.7). Total num frames: 1077248. Throughput: 0: 971.6. Samples: 267448. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:13:43,995][00405] Avg episode reward: [(0, '4.611')]
[2023-02-23 09:13:48,988][00405] Fps is (10 sec: 3686.3, 60 sec: 3754.7, 300 sec: 3693.3). Total num frames: 1089536. Throughput: 0: 935.0. Samples: 272824. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:13:48,995][00405] Avg episode reward: [(0, '4.758')]
[2023-02-23 09:13:53,970][26325] Updated weights for policy 0, policy_version 270 (0.0015)
[2023-02-23 09:13:53,987][00405] Fps is (10 sec: 2867.2, 60 sec: 3822.9, 300 sec: 3748.9). Total num frames: 1105920. Throughput: 0: 922.2. Samples: 277166. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:13:53,994][00405] Avg episode reward: [(0, '4.686')]
[2023-02-23 09:13:58,987][00405] Fps is (10 sec: 3686.5, 60 sec: 3754.7, 300 sec: 3762.8). Total num frames: 1126400. Throughput: 0: 950.2. Samples: 280630. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:13:58,993][00405] Avg episode reward: [(0, '4.873')]
[2023-02-23 09:14:02,754][26325] Updated weights for policy 0, policy_version 280 (0.0020)
[2023-02-23 09:14:03,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3823.0, 300 sec: 3790.6). Total num frames: 1150976. Throughput: 0: 969.3. Samples: 287584. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
[2023-02-23 09:14:03,990][00405] Avg episode reward: [(0, '4.868')]
[2023-02-23 09:14:08,988][00405] Fps is (10 sec: 4095.8, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 1167360. Throughput: 0: 922.2. Samples: 292422. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:14:08,991][00405] Avg episode reward: [(0, '4.853')]
[2023-02-23 09:14:13,987][00405] Fps is (10 sec: 2867.2, 60 sec: 3754.8, 300 sec: 3762.8). Total num frames: 1179648. Throughput: 0: 920.5. Samples: 294580. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:14:13,990][00405] Avg episode reward: [(0, '5.107')]
[2023-02-23 09:14:14,107][26306] Saving new best policy, reward=5.107!
[2023-02-23 09:14:15,053][26325] Updated weights for policy 0, policy_version 290 (0.0018)
[2023-02-23 09:14:18,988][00405] Fps is (10 sec: 3686.5, 60 sec: 3822.9, 300 sec: 3776.7). Total num frames: 1204224. Throughput: 0: 962.1. Samples: 300814. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:14:18,995][00405] Avg episode reward: [(0, '5.237')]
[2023-02-23 09:14:19,007][26306] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000294_1204224.pth...
[2023-02-23 09:14:19,133][26306] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000073_299008.pth
[2023-02-23 09:14:19,156][26306] Saving new best policy, reward=5.237!
[2023-02-23 09:14:23,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3754.7, 300 sec: 3776.7). Total num frames: 1224704. Throughput: 0: 966.0. Samples: 307412. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:14:23,990][00405] Avg episode reward: [(0, '5.388')]
[2023-02-23 09:14:23,993][26306] Saving new best policy, reward=5.388!
[2023-02-23 09:14:24,459][26325] Updated weights for policy 0, policy_version 300 (0.0012)
[2023-02-23 09:14:28,989][00405] Fps is (10 sec: 3686.0, 60 sec: 3754.7, 300 sec: 3790.5). Total num frames: 1241088. Throughput: 0: 936.3. Samples: 309584. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:14:28,992][00405] Avg episode reward: [(0, '5.367')]
[2023-02-23 09:14:33,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3823.0, 300 sec: 3776.7). Total num frames: 1257472. Throughput: 0: 912.9. Samples: 313904. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:14:33,996][00405] Avg episode reward: [(0, '5.530')]
[2023-02-23 09:14:34,000][26306] Saving new best policy, reward=5.530!
[2023-02-23 09:14:36,593][26325] Updated weights for policy 0, policy_version 310 (0.0014)
[2023-02-23 09:14:38,987][00405] Fps is (10 sec: 3686.9, 60 sec: 3754.7, 300 sec: 3762.8). Total num frames: 1277952. Throughput: 0: 968.4. Samples: 320742. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:14:38,990][00405] Avg episode reward: [(0, '5.495')]
[2023-02-23 09:14:43,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3776.7). Total num frames: 1298432. Throughput: 0: 969.2. Samples: 324244. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:14:43,990][00405] Avg episode reward: [(0, '5.428')]
[2023-02-23 09:14:46,722][26325] Updated weights for policy 0, policy_version 320 (0.0015)
[2023-02-23 09:14:48,988][00405] Fps is (10 sec: 3686.3, 60 sec: 3754.7, 300 sec: 3776.6). Total num frames: 1314816. Throughput: 0: 923.1. Samples: 329126. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:14:48,990][00405] Avg episode reward: [(0, '5.223')]
[2023-02-23 09:14:53,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3762.8). Total num frames: 1331200. Throughput: 0: 920.6. Samples: 333850. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:14:53,990][00405] Avg episode reward: [(0, '5.434')]
[2023-02-23 09:14:57,819][26325] Updated weights for policy 0, policy_version 330 (0.0028)
[2023-02-23 09:14:58,987][00405] Fps is (10 sec: 4096.1, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 1355776. Throughput: 0: 951.7. Samples: 337406. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:14:58,989][00405] Avg episode reward: [(0, '5.900')]
[2023-02-23 09:14:59,004][26306] Saving new best policy, reward=5.900!
[2023-02-23 09:15:03,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3754.7, 300 sec: 3776.7). Total num frames: 1376256. Throughput: 0: 970.4. Samples: 344482. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:15:03,998][00405] Avg episode reward: [(0, '5.871')]
[2023-02-23 09:15:08,468][26325] Updated weights for policy 0, policy_version 340 (0.0014)
[2023-02-23 09:15:08,990][00405] Fps is (10 sec: 3685.5, 60 sec: 3754.6, 300 sec: 3790.5). Total num frames: 1392640. Throughput: 0: 923.5. Samples: 348972. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:15:08,994][00405] Avg episode reward: [(0, '5.955')]
[2023-02-23 09:15:09,011][26306] Saving new best policy, reward=5.955!
[2023-02-23 09:15:13,988][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 1409024. Throughput: 0: 923.4. Samples: 351138. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:15:13,990][00405] Avg episode reward: [(0, '5.944')]
[2023-02-23 09:15:18,987][00405] Fps is (10 sec: 3687.3, 60 sec: 3754.7, 300 sec: 3762.8). Total num frames: 1429504. Throughput: 0: 974.7. Samples: 357766. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:15:18,989][00405] Avg episode reward: [(0, '5.918')]
[2023-02-23 09:15:19,098][26325] Updated weights for policy 0, policy_version 350 (0.0017)
[2023-02-23 09:15:23,987][00405] Fps is (10 sec: 4505.7, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 1454080. Throughput: 0: 968.3. Samples: 364314. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:15:23,991][00405] Avg episode reward: [(0, '5.568')]
[2023-02-23 09:15:28,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3790.5). Total num frames: 1466368. Throughput: 0: 939.6. Samples: 366524. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
[2023-02-23 09:15:28,995][00405] Avg episode reward: [(0, '5.346')]
[2023-02-23 09:15:30,649][26325] Updated weights for policy 0, policy_version 360 (0.0015)
[2023-02-23 09:15:33,987][00405] Fps is (10 sec: 2867.2, 60 sec: 3754.7, 300 sec: 3762.8). Total num frames: 1482752. Throughput: 0: 929.7. Samples: 370964. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:15:33,994][00405] Avg episode reward: [(0, '5.363')]
[2023-02-23 09:15:38,988][00405] Fps is (10 sec: 4095.9, 60 sec: 3822.9, 300 sec: 3776.6). Total num frames: 1507328. Throughput: 0: 979.7. Samples: 377936. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:15:38,994][00405] Avg episode reward: [(0, '5.415')]
[2023-02-23 09:15:40,298][26325] Updated weights for policy 0, policy_version 370 (0.0047)
[2023-02-23 09:15:43,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 1527808. Throughput: 0: 978.4. Samples: 381432. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:15:43,990][00405] Avg episode reward: [(0, '5.763')]
[2023-02-23 09:15:48,987][00405] Fps is (10 sec: 3686.5, 60 sec: 3823.0, 300 sec: 3804.4). Total num frames: 1544192. Throughput: 0: 929.4. Samples: 386304. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-23 09:15:48,992][00405] Avg episode reward: [(0, '5.694')]
[2023-02-23 09:15:52,453][26325] Updated weights for policy 0, policy_version 380 (0.0032)
[2023-02-23 09:15:53,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3776.7). Total num frames: 1560576. Throughput: 0: 939.6. Samples: 391254. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:15:53,995][00405] Avg episode reward: [(0, '5.664')]
[2023-02-23 09:15:58,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3776.6). Total num frames: 1585152. Throughput: 0: 969.1. Samples: 394748. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:15:58,989][00405] Avg episode reward: [(0, '6.080')]
[2023-02-23 09:15:59,004][26306] Saving new best policy, reward=6.080!
[2023-02-23 09:16:01,365][26325] Updated weights for policy 0, policy_version 390 (0.0021)
[2023-02-23 09:16:03,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3822.9, 300 sec: 3804.4). Total num frames: 1605632. Throughput: 0: 975.2. Samples: 401650. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:16:03,994][00405] Avg episode reward: [(0, '6.137')]
[2023-02-23 09:16:03,996][26306] Saving new best policy, reward=6.137!
[2023-02-23 09:16:08,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3754.8, 300 sec: 3790.5). Total num frames: 1617920. Throughput: 0: 923.8. Samples: 405886. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:16:08,992][00405] Avg episode reward: [(0, '6.045')]
[2023-02-23 09:16:13,785][26325] Updated weights for policy 0, policy_version 400 (0.0019)
[2023-02-23 09:16:13,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3776.7). Total num frames: 1638400. Throughput: 0: 924.1. Samples: 408108. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-23 09:16:13,993][00405] Avg episode reward: [(0, '5.861')]
[2023-02-23 09:16:18,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3776.7). Total num frames: 1658880. Throughput: 0: 976.4. Samples: 414902. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-23 09:16:18,995][00405] Avg episode reward: [(0, '6.110')]
[2023-02-23 09:16:19,013][26306] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000405_1658880.pth...
[2023-02-23 09:16:19,158][26306] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000183_749568.pth
[2023-02-23 09:16:22,804][26325] Updated weights for policy 0, policy_version 410 (0.0030)
[2023-02-23 09:16:23,989][00405] Fps is (10 sec: 4095.5, 60 sec: 3754.6, 300 sec: 3790.5). Total num frames: 1679360. Throughput: 0: 960.9. Samples: 421178. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:16:23,995][00405] Avg episode reward: [(0, '6.379')]
[2023-02-23 09:16:23,999][26306] Saving new best policy, reward=6.379!
[2023-02-23 09:16:28,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3804.4). Total num frames: 1695744. Throughput: 0: 930.6. Samples: 423310. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:16:28,994][00405] Avg episode reward: [(0, '6.547')]
[2023-02-23 09:16:29,008][26306] Saving new best policy, reward=6.547!
[2023-02-23 09:16:33,987][00405] Fps is (10 sec: 3277.2, 60 sec: 3822.9, 300 sec: 3776.7). Total num frames: 1712128. Throughput: 0: 922.0. Samples: 427794. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:16:33,994][00405] Avg episode reward: [(0, '6.137')]
[2023-02-23 09:16:35,290][26325] Updated weights for policy 0, policy_version 420 (0.0018)
[2023-02-23 09:16:38,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3823.0, 300 sec: 3776.7). Total num frames: 1736704. Throughput: 0: 968.7. Samples: 434846. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:16:38,993][00405] Avg episode reward: [(0, '6.604')]
[2023-02-23 09:16:39,007][26306] Saving new best policy, reward=6.604!
[2023-02-23 09:16:43,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3822.9, 300 sec: 3804.4). Total num frames: 1757184. Throughput: 0: 968.2. Samples: 438316. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:16:43,992][00405] Avg episode reward: [(0, '7.178')]
[2023-02-23 09:16:43,998][26306] Saving new best policy, reward=7.178!
[2023-02-23 09:16:44,874][26325] Updated weights for policy 0, policy_version 430 (0.0013)
[2023-02-23 09:16:48,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3790.5). Total num frames: 1769472. Throughput: 0: 918.2. Samples: 442968. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-23 09:16:48,991][00405] Avg episode reward: [(0, '7.323')]
[2023-02-23 09:16:49,022][26306] Saving new best policy, reward=7.323!
[2023-02-23 09:16:53,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 1789952. Throughput: 0: 933.8. Samples: 447906. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0)
[2023-02-23 09:16:53,995][00405] Avg episode reward: [(0, '7.629')]
[2023-02-23 09:16:53,998][26306] Saving new best policy, reward=7.629!
[2023-02-23 09:16:56,687][26325] Updated weights for policy 0, policy_version 440 (0.0021)
[2023-02-23 09:16:58,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3776.7). Total num frames: 1810432. Throughput: 0: 961.6. Samples: 451378. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:16:58,993][00405] Avg episode reward: [(0, '8.533')]
[2023-02-23 09:16:59,004][26306] Saving new best policy, reward=8.533!
[2023-02-23 09:17:03,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3790.5). Total num frames: 1830912. Throughput: 0: 962.4. Samples: 458208. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:17:03,994][00405] Avg episode reward: [(0, '8.077')]
[2023-02-23 09:17:07,444][26325] Updated weights for policy 0, policy_version 450 (0.0015)
[2023-02-23 09:17:08,989][00405] Fps is (10 sec: 3685.7, 60 sec: 3822.8, 300 sec: 3804.4). Total num frames: 1847296. Throughput: 0: 920.2. Samples: 462588. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:17:08,993][00405] Avg episode reward: [(0, '8.046')]
[2023-02-23 09:17:13,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3776.7). Total num frames: 1863680. Throughput: 0: 922.0. Samples: 464802. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:17:13,996][00405] Avg episode reward: [(0, '8.015')]
[2023-02-23 09:17:18,032][26325] Updated weights for policy 0, policy_version 460 (0.0026)
[2023-02-23 09:17:18,987][00405] Fps is (10 sec: 4096.7, 60 sec: 3822.9, 300 sec: 3790.6). Total num frames: 1888256. Throughput: 0: 973.4. Samples: 471598. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:17:18,995][00405] Avg episode reward: [(0, '8.510')]
[2023-02-23 09:17:23,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3823.0, 300 sec: 3804.4). Total num frames: 1908736. Throughput: 0: 955.5. Samples: 477842. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:17:23,991][00405] Avg episode reward: [(0, '8.454')]
[2023-02-23 09:17:28,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3790.5). Total num frames: 1921024. Throughput: 0: 927.9. Samples: 480070. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:17:28,995][00405] Avg episode reward: [(0, '8.812')]
[2023-02-23 09:17:29,007][26306] Saving new best policy, reward=8.812!
[2023-02-23 09:17:29,473][26325] Updated weights for policy 0, policy_version 470 (0.0016)
[2023-02-23 09:17:33,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 1941504. Throughput: 0: 929.2. Samples: 484784. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:17:33,990][00405] Avg episode reward: [(0, '9.310')]
[2023-02-23 09:17:33,992][26306] Saving new best policy, reward=9.310!
[2023-02-23 09:17:38,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3776.7). Total num frames: 1961984. Throughput: 0: 973.1. Samples: 491696. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:17:38,990][00405] Avg episode reward: [(0, '10.081')]
[2023-02-23 09:17:38,998][26306] Saving new best policy, reward=10.081!
[2023-02-23 09:17:39,340][26325] Updated weights for policy 0, policy_version 480 (0.0035)
[2023-02-23 09:17:43,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3790.5). Total num frames: 1982464. Throughput: 0: 972.2. Samples: 495128. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:17:43,991][00405] Avg episode reward: [(0, '10.410')]
[2023-02-23 09:17:43,996][26306] Saving new best policy, reward=10.410!
[2023-02-23 09:17:48,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3804.4). Total num frames: 1998848. Throughput: 0: 923.0. Samples: 499744. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:17:48,990][00405] Avg episode reward: [(0, '11.194')]
[2023-02-23 09:17:49,000][26306] Saving new best policy, reward=11.194!
[2023-02-23 09:17:51,582][26325] Updated weights for policy 0, policy_version 490 (0.0020)
[2023-02-23 09:17:53,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3776.7). Total num frames: 2015232. Throughput: 0: 936.9. Samples: 504748. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:17:53,990][00405] Avg episode reward: [(0, '11.468')]
[2023-02-23 09:17:53,997][26306] Saving new best policy, reward=11.468!
[2023-02-23 09:17:58,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3776.7). Total num frames: 2035712. Throughput: 0: 963.9. Samples: 508176. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:17:58,990][00405] Avg episode reward: [(0, '11.245')]
[2023-02-23 09:18:00,757][26325] Updated weights for policy 0, policy_version 500 (0.0023)
[2023-02-23 09:18:03,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3822.9, 300 sec: 3804.4). Total num frames: 2060288. Throughput: 0: 961.0. Samples: 514842. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:18:03,990][00405] Avg episode reward: [(0, '11.309')]
[2023-02-23 09:18:08,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3754.8, 300 sec: 3790.6). Total num frames: 2072576. Throughput: 0: 920.4. Samples: 519262. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:18:08,994][00405] Avg episode reward: [(0, '10.986')]
[2023-02-23 09:18:13,146][26325] Updated weights for policy 0, policy_version 510 (0.0023)
[2023-02-23 09:18:13,987][00405] Fps is (10 sec: 2867.2, 60 sec: 3754.7, 300 sec: 3776.7). Total num frames: 2088960. Throughput: 0: 922.2. Samples: 521568. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:18:13,990][00405] Avg episode reward: [(0, '11.574')]
[2023-02-23 09:18:13,994][26306] Saving new best policy, reward=11.574!
[2023-02-23 09:18:18,988][00405] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3776.6). Total num frames: 2113536. Throughput: 0: 967.2. Samples: 528308. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:18:18,989][00405] Avg episode reward: [(0, '12.367')]
[2023-02-23 09:18:19,004][26306] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000516_2113536.pth...
[2023-02-23 09:18:19,139][26306] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000294_1204224.pth
[2023-02-23 09:18:19,150][26306] Saving new best policy, reward=12.367!
[2023-02-23 09:18:22,211][26325] Updated weights for policy 0, policy_version 520 (0.0017)
[2023-02-23 09:18:23,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3754.7, 300 sec: 3790.6). Total num frames: 2134016. Throughput: 0: 949.9. Samples: 534442. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
[2023-02-23 09:18:23,991][00405] Avg episode reward: [(0, '12.556')]
[2023-02-23 09:18:23,993][26306] Saving new best policy, reward=12.556!
[2023-02-23 09:18:28,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3790.5). Total num frames: 2146304. Throughput: 0: 921.7. Samples: 536604. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:18:28,989][00405] Avg episode reward: [(0, '13.107')]
[2023-02-23 09:18:29,012][26306] Saving new best policy, reward=13.107!
[2023-02-23 09:18:33,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3776.7). Total num frames: 2166784. Throughput: 0: 920.1. Samples: 541150. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:18:33,994][00405] Avg episode reward: [(0, '12.446')]
[2023-02-23 09:18:34,718][26325] Updated weights for policy 0, policy_version 530 (0.0025)
[2023-02-23 09:18:38,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3762.8). Total num frames: 2187264. Throughput: 0: 965.1. Samples: 548178. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:18:38,996][00405] Avg episode reward: [(0, '12.858')]
[2023-02-23 09:18:43,992][00405] Fps is (10 sec: 4094.1, 60 sec: 3754.4, 300 sec: 3790.5). Total num frames: 2207744. Throughput: 0: 966.4. Samples: 551668. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:18:43,995][00405] Avg episode reward: [(0, '12.089')]
[2023-02-23 09:18:44,054][26325] Updated weights for policy 0, policy_version 540 (0.0023)
[2023-02-23 09:18:48,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3790.5). Total num frames: 2224128. Throughput: 0: 922.8. Samples: 556370. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:18:48,989][00405] Avg episode reward: [(0, '12.455')]
[2023-02-23 09:18:53,987][00405] Fps is (10 sec: 3278.3, 60 sec: 3754.7, 300 sec: 3776.7). Total num frames: 2240512. Throughput: 0: 937.4. Samples: 561444. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:18:53,994][00405] Avg episode reward: [(0, '11.644')]
[2023-02-23 09:18:55,906][26325] Updated weights for policy 0, policy_version 550 (0.0012)
[2023-02-23 09:18:58,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3776.6). Total num frames: 2265088. Throughput: 0: 963.5. Samples: 564926. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:18:58,994][00405] Avg episode reward: [(0, '12.889')]
[2023-02-23 09:19:03,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3754.7, 300 sec: 3790.5). Total num frames: 2285568. Throughput: 0: 966.3. Samples: 571790. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:19:03,990][00405] Avg episode reward: [(0, '13.089')]
[2023-02-23 09:19:05,944][26325] Updated weights for policy 0, policy_version 560 (0.0012)
[2023-02-23 09:19:08,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3804.4). Total num frames: 2301952. Throughput: 0: 927.3. Samples: 576172. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:19:08,997][00405] Avg episode reward: [(0, '13.608')]
[2023-02-23 09:19:09,015][26306] Saving new best policy, reward=13.608!
[2023-02-23 09:19:13,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3776.7). Total num frames: 2318336. Throughput: 0: 927.6. Samples: 578346. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:19:13,989][00405] Avg episode reward: [(0, '14.533')]
[2023-02-23 09:19:13,995][26306] Saving new best policy, reward=14.533!
[2023-02-23 09:19:17,146][26325] Updated weights for policy 0, policy_version 570 (0.0022)
[2023-02-23 09:19:18,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 2342912. Throughput: 0: 977.6. Samples: 585144. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:19:18,992][00405] Avg episode reward: [(0, '14.009')]
[2023-02-23 09:19:23,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3822.9, 300 sec: 3804.4). Total num frames: 2363392. Throughput: 0: 964.6. Samples: 591584. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:19:23,994][00405] Avg episode reward: [(0, '12.611')]
[2023-02-23 09:19:27,891][26325] Updated weights for policy 0, policy_version 580 (0.0012)
[2023-02-23 09:19:28,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 2375680. Throughput: 0: 935.2. Samples: 593746. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:19:29,003][00405] Avg episode reward: [(0, '11.680')]
[2023-02-23 09:19:33,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 2396160. Throughput: 0: 935.6. Samples: 598474. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:19:33,992][00405] Avg episode reward: [(0, '11.523')]
[2023-02-23 09:19:38,251][26325] Updated weights for policy 0, policy_version 590 (0.0020)
[2023-02-23 09:19:38,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 2416640. Throughput: 0: 981.1. Samples: 605592. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:19:38,990][00405] Avg episode reward: [(0, '12.911')]
[2023-02-23 09:19:43,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3823.2, 300 sec: 3804.4). Total num frames: 2437120. Throughput: 0: 980.8. Samples: 609064. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:19:43,996][00405] Avg episode reward: [(0, '13.728')]
[2023-02-23 09:19:48,989][00405] Fps is (10 sec: 3685.8, 60 sec: 3822.8, 300 sec: 3804.4). Total num frames: 2453504. Throughput: 0: 932.9. Samples: 613772. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:19:48,991][00405] Avg episode reward: [(0, '14.933')]
[2023-02-23 09:19:49,002][26306] Saving new best policy, reward=14.933!
[2023-02-23 09:19:49,658][26325] Updated weights for policy 0, policy_version 600 (0.0011)
[2023-02-23 09:19:53,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3776.7). Total num frames: 2469888. Throughput: 0: 951.6. Samples: 618994. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:19:53,990][00405] Avg episode reward: [(0, '16.287')]
[2023-02-23 09:19:54,026][26306] Saving new best policy, reward=16.287!
[2023-02-23 09:19:58,987][00405] Fps is (10 sec: 4096.7, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 2494464. Throughput: 0: 977.9. Samples: 622350. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:19:58,995][00405] Avg episode reward: [(0, '16.262')]
[2023-02-23 09:19:59,303][26325] Updated weights for policy 0, policy_version 610 (0.0016)
[2023-02-23 09:20:03,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3822.9, 300 sec: 3804.5). Total num frames: 2514944. Throughput: 0: 976.2. Samples: 629074. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:20:03,990][00405] Avg episode reward: [(0, '16.551')]
[2023-02-23 09:20:03,995][26306] Saving new best policy, reward=16.551!
[2023-02-23 09:20:08,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3804.4). Total num frames: 2531328. Throughput: 0: 929.1. Samples: 633392. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:20:08,992][00405] Avg episode reward: [(0, '16.697')]
[2023-02-23 09:20:09,003][26306] Saving new best policy, reward=16.697!
[2023-02-23 09:20:11,733][26325] Updated weights for policy 0, policy_version 620 (0.0012)
[2023-02-23 09:20:13,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 2547712. Throughput: 0: 930.1. Samples: 635602. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:20:13,996][00405] Avg episode reward: [(0, '16.591')]
[2023-02-23 09:20:18,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 2572288. Throughput: 0: 980.6. Samples: 642600. Policy #0 lag: (min: 0.0, avg: 0.8, max: 2.0)
[2023-02-23 09:20:18,990][00405] Avg episode reward: [(0, '16.503')]
[2023-02-23 09:20:19,001][26306] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000628_2572288.pth...
[2023-02-23 09:20:19,113][26306] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000405_1658880.pth
[2023-02-23 09:20:20,781][26325] Updated weights for policy 0, policy_version 630 (0.0016)
[2023-02-23 09:20:23,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3804.4). Total num frames: 2588672. Throughput: 0: 959.7. Samples: 648780. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
[2023-02-23 09:20:23,993][00405] Avg episode reward: [(0, '15.285')]
[2023-02-23 09:20:28,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3804.4). Total num frames: 2605056. Throughput: 0: 929.6. Samples: 650894. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:20:28,990][00405] Avg episode reward: [(0, '15.006')]
[2023-02-23 09:20:33,150][26325] Updated weights for policy 0, policy_version 640 (0.0022)
[2023-02-23 09:20:33,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3776.7). Total num frames: 2621440. Throughput: 0: 930.7. Samples: 655654. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:20:33,993][00405] Avg episode reward: [(0, '16.280')]
[2023-02-23 09:20:38,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 2646016. Throughput: 0: 971.7. Samples: 662722. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:20:38,989][00405] Avg episode reward: [(0, '17.089')]
[2023-02-23 09:20:39,006][26306] Saving new best policy, reward=17.089!
[2023-02-23 09:20:41,858][26325] Updated weights for policy 0, policy_version 650 (0.0014)
[2023-02-23 09:20:43,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3822.9, 300 sec: 3804.4). Total num frames: 2666496. Throughput: 0: 974.5. Samples: 666202. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:20:43,998][00405] Avg episode reward: [(0, '17.846')]
[2023-02-23 09:20:44,009][26306] Saving new best policy, reward=17.846!
[2023-02-23 09:20:48,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3823.0, 300 sec: 3804.4). Total num frames: 2682880. Throughput: 0: 923.4. Samples: 670626. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:20:48,990][00405] Avg episode reward: [(0, '19.909')]
[2023-02-23 09:20:49,010][26306] Saving new best policy, reward=19.909!
[2023-02-23 09:20:53,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3776.7). Total num frames: 2699264. Throughput: 0: 945.1. Samples: 675920. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:20:53,995][00405] Avg episode reward: [(0, '20.034')]
[2023-02-23 09:20:54,003][26306] Saving new best policy, reward=20.034!
[2023-02-23 09:20:54,506][26325] Updated weights for policy 0, policy_version 660 (0.0014)
[2023-02-23 09:20:58,988][00405] Fps is (10 sec: 4095.9, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 2723840. Throughput: 0: 970.7. Samples: 679282. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:20:58,990][00405] Avg episode reward: [(0, '19.638')]
[2023-02-23 09:21:03,988][00405] Fps is (10 sec: 4095.8, 60 sec: 3754.6, 300 sec: 3804.4). Total num frames: 2740224. Throughput: 0: 958.5. Samples: 685734. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:21:03,993][00405] Avg episode reward: [(0, '19.575')]
[2023-02-23 09:21:04,058][26325] Updated weights for policy 0, policy_version 670 (0.0013)
[2023-02-23 09:21:08,992][00405] Fps is (10 sec: 3275.4, 60 sec: 3754.4, 300 sec: 3790.5). Total num frames: 2756608. Throughput: 0: 921.8. Samples: 690264. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:21:08,995][00405] Avg episode reward: [(0, '18.543')]
[2023-02-23 09:21:13,987][00405] Fps is (10 sec: 3686.6, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 2777088. Throughput: 0: 926.1. Samples: 692568. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:21:13,989][00405] Avg episode reward: [(0, '19.222')]
[2023-02-23 09:21:15,642][26325] Updated weights for policy 0, policy_version 680 (0.0016)
[2023-02-23 09:21:18,987][00405] Fps is (10 sec: 4097.9, 60 sec: 3754.7, 300 sec: 3790.6). Total num frames: 2797568. Throughput: 0: 976.2. Samples: 699582. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:21:18,990][00405] Avg episode reward: [(0, '20.939')]
[2023-02-23 09:21:19,010][26306] Saving new best policy, reward=20.939!
[2023-02-23 09:21:23,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3804.4). Total num frames: 2818048. Throughput: 0: 952.2. Samples: 705572. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:21:23,994][00405] Avg episode reward: [(0, '20.009')]
[2023-02-23 09:21:26,141][26325] Updated weights for policy 0, policy_version 690 (0.0030)
[2023-02-23 09:21:28,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3804.4). Total num frames: 2834432. Throughput: 0: 924.0. Samples: 707784. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:21:28,995][00405] Avg episode reward: [(0, '19.946')]
[2023-02-23 09:21:33,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3776.7). Total num frames: 2850816. Throughput: 0: 933.9. Samples: 712650. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:21:33,995][00405] Avg episode reward: [(0, '18.794')]
[2023-02-23 09:21:37,066][26325] Updated weights for policy 0, policy_version 700 (0.0032)
[2023-02-23 09:21:38,988][00405] Fps is (10 sec: 4095.9, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 2875392. Throughput: 0: 972.8. Samples: 719694. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:21:38,990][00405] Avg episode reward: [(0, '18.287')]
[2023-02-23 09:21:43,989][00405] Fps is (10 sec: 4504.8, 60 sec: 3822.8, 300 sec: 3818.3). Total num frames: 2895872. Throughput: 0: 974.5. Samples: 723138. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:21:43,992][00405] Avg episode reward: [(0, '18.491')]
[2023-02-23 09:21:48,007][26325] Updated weights for policy 0, policy_version 710 (0.0032)
[2023-02-23 09:21:48,987][00405] Fps is (10 sec: 3276.9, 60 sec: 3754.7, 300 sec: 3790.5). Total num frames: 2908160. Throughput: 0: 931.7. Samples: 727658. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:21:48,994][00405] Avg episode reward: [(0, '19.062')]
[2023-02-23 09:21:53,988][00405] Fps is (10 sec: 3277.2, 60 sec: 3822.9, 300 sec: 3790.5). Total num frames: 2928640. Throughput: 0: 951.6. Samples: 733082. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:21:53,996][00405] Avg episode reward: [(0, '19.157')]
[2023-02-23 09:21:58,020][26325] Updated weights for policy 0, policy_version 720 (0.0012)
[2023-02-23 09:21:58,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3822.9, 300 sec: 3804.4). Total num frames: 2953216. Throughput: 0: 976.4. Samples: 736506. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:21:58,995][00405] Avg episode reward: [(0, '19.738')]
[2023-02-23 09:22:03,987][00405] Fps is (10 sec: 4096.2, 60 sec: 3823.0, 300 sec: 3804.4). Total num frames: 2969600. Throughput: 0: 954.3. Samples: 742524. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:22:03,997][00405] Avg episode reward: [(0, '20.591')]
[2023-02-23 09:22:08,995][00405] Fps is (10 sec: 2865.0, 60 sec: 3754.5, 300 sec: 3790.4). Total num frames: 2981888. Throughput: 0: 914.2. Samples: 746718. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:22:09,005][00405] Avg episode reward: [(0, '19.966')]
[2023-02-23 09:22:10,846][26325] Updated weights for policy 0, policy_version 730 (0.0039)
[2023-02-23 09:22:13,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3776.7). Total num frames: 3002368. Throughput: 0: 916.0. Samples: 749002. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:22:13,990][00405] Avg episode reward: [(0, '20.090')]
[2023-02-23 09:22:18,988][00405] Fps is (10 sec: 4099.1, 60 sec: 3754.7, 300 sec: 3776.6). Total num frames: 3022848. Throughput: 0: 961.8. Samples: 755930. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:22:18,990][00405] Avg episode reward: [(0, '20.286')]
[2023-02-23 09:22:19,002][26306] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000738_3022848.pth...
[2023-02-23 09:22:19,140][26306] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000516_2113536.pth
[2023-02-23 09:22:20,185][26325] Updated weights for policy 0, policy_version 740 (0.0013)
[2023-02-23 09:22:23,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3804.4). Total num frames: 3043328. Throughput: 0: 929.3. Samples: 761512. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:22:23,994][00405] Avg episode reward: [(0, '21.542')]
[2023-02-23 09:22:23,996][26306] Saving new best policy, reward=21.542!
[2023-02-23 09:22:28,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3686.4, 300 sec: 3776.7). Total num frames: 3055616. Throughput: 0: 896.4. Samples: 763474. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:22:28,999][00405] Avg episode reward: [(0, '21.851')]
[2023-02-23 09:22:29,023][26306] Saving new best policy, reward=21.851!
[2023-02-23 09:22:33,666][26325] Updated weights for policy 0, policy_version 750 (0.0012)
[2023-02-23 09:22:33,987][00405] Fps is (10 sec: 2867.2, 60 sec: 3686.4, 300 sec: 3762.8). Total num frames: 3072000. Throughput: 0: 889.1. Samples: 767666. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:22:33,992][00405] Avg episode reward: [(0, '21.717')]
[2023-02-23 09:22:38,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3618.1, 300 sec: 3762.8). Total num frames: 3092480. Throughput: 0: 914.1. Samples: 774218. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:22:38,990][00405] Avg episode reward: [(0, '23.117')]
[2023-02-23 09:22:38,998][26306] Saving new best policy, reward=23.117!
[2023-02-23 09:22:43,665][26325] Updated weights for policy 0, policy_version 760 (0.0032)
[2023-02-23 09:22:43,988][00405] Fps is (10 sec: 4095.9, 60 sec: 3618.2, 300 sec: 3776.6). Total num frames: 3112960. Throughput: 0: 909.0. Samples: 777410. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:22:43,990][00405] Avg episode reward: [(0, '23.439')]
[2023-02-23 09:22:43,999][26306] Saving new best policy, reward=23.439!
[2023-02-23 09:22:48,990][00405] Fps is (10 sec: 3276.1, 60 sec: 3618.0, 300 sec: 3762.7). Total num frames: 3125248. Throughput: 0: 875.7. Samples: 781934. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:22:48,998][00405] Avg episode reward: [(0, '22.888')]
[2023-02-23 09:22:53,987][00405] Fps is (10 sec: 3276.9, 60 sec: 3618.2, 300 sec: 3762.8). Total num frames: 3145728. Throughput: 0: 895.8. Samples: 787022. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:22:53,995][00405] Avg episode reward: [(0, '22.342')]
[2023-02-23 09:22:55,754][26325] Updated weights for policy 0, policy_version 770 (0.0021)
[2023-02-23 09:22:58,987][00405] Fps is (10 sec: 4096.9, 60 sec: 3549.9, 300 sec: 3748.9). Total num frames: 3166208. Throughput: 0: 922.5. Samples: 790516. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:22:58,996][00405] Avg episode reward: [(0, '21.496')]
[2023-02-23 09:23:03,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3618.1, 300 sec: 3776.7). Total num frames: 3186688. Throughput: 0: 916.2. Samples: 797158. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:23:03,991][00405] Avg episode reward: [(0, '20.006')]
[2023-02-23 09:23:05,712][26325] Updated weights for policy 0, policy_version 780 (0.0014)
[2023-02-23 09:23:08,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3686.9, 300 sec: 3776.6). Total num frames: 3203072. Throughput: 0: 888.4. Samples: 801490. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-23 09:23:08,994][00405] Avg episode reward: [(0, '19.793')]
[2023-02-23 09:23:13,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3618.1, 300 sec: 3748.9). Total num frames: 3219456. Throughput: 0: 893.8. Samples: 803694. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:23:13,993][00405] Avg episode reward: [(0, '20.353')]
[2023-02-23 09:23:17,044][26325] Updated weights for policy 0, policy_version 790 (0.0015)
[2023-02-23 09:23:18,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3686.4, 300 sec: 3762.8). Total num frames: 3244032. Throughput: 0: 955.2. Samples: 810648. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:23:18,989][00405] Avg episode reward: [(0, '19.738')]
[2023-02-23 09:23:23,998][00405] Fps is (10 sec: 4500.9, 60 sec: 3685.8, 300 sec: 3790.4). Total num frames: 3264512. Throughput: 0: 943.9. Samples: 816704. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:23:24,004][00405] Avg episode reward: [(0, '20.272')]
[2023-02-23 09:23:28,445][26325] Updated weights for policy 0, policy_version 800 (0.0024)
[2023-02-23 09:23:28,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3686.4, 300 sec: 3762.8). Total num frames: 3276800. Throughput: 0: 920.9. Samples: 818850. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:23:28,990][00405] Avg episode reward: [(0, '20.611')]
[2023-02-23 09:23:33,987][00405] Fps is (10 sec: 2870.2, 60 sec: 3686.4, 300 sec: 3748.9). Total num frames: 3293184. Throughput: 0: 925.6. Samples: 823582. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:23:33,990][00405] Avg episode reward: [(0, '20.386')]
[2023-02-23 09:23:38,550][26325] Updated weights for policy 0, policy_version 810 (0.0016)
[2023-02-23 09:23:38,988][00405] Fps is (10 sec: 4095.9, 60 sec: 3754.6, 300 sec: 3762.8). Total num frames: 3317760. Throughput: 0: 966.6. Samples: 830518. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:23:38,995][00405] Avg episode reward: [(0, '21.679')]
[2023-02-23 09:23:43,993][00405] Fps is (10 sec: 4503.0, 60 sec: 3754.3, 300 sec: 3776.6). Total num frames: 3338240. Throughput: 0: 968.9. Samples: 834120. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:23:43,996][00405] Avg episode reward: [(0, '20.948')]
[2023-02-23 09:23:48,987][00405] Fps is (10 sec: 3276.9, 60 sec: 3754.8, 300 sec: 3762.8). Total num frames: 3350528. Throughput: 0: 917.2. Samples: 838432. Policy #0 lag: (min: 0.0, avg: 0.7, max: 1.0)
[2023-02-23 09:23:48,994][00405] Avg episode reward: [(0, '21.569')]
[2023-02-23 09:23:50,590][26325] Updated weights for policy 0, policy_version 820 (0.0022)
[2023-02-23 09:23:53,987][00405] Fps is (10 sec: 3278.7, 60 sec: 3754.7, 300 sec: 3748.9). Total num frames: 3371008. Throughput: 0: 935.8. Samples: 843600. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:23:53,990][00405] Avg episode reward: [(0, '22.374')]
[2023-02-23 09:23:58,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3748.9). Total num frames: 3391488. Throughput: 0: 965.8. Samples: 847154. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:23:58,990][00405] Avg episode reward: [(0, '23.679')]
[2023-02-23 09:23:59,010][26306] Saving new best policy, reward=23.679!
[2023-02-23 09:23:59,888][26325] Updated weights for policy 0, policy_version 830 (0.0018)
[2023-02-23 09:24:03,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3762.8). Total num frames: 3411968. Throughput: 0: 956.8. Samples: 853702. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:24:03,995][00405] Avg episode reward: [(0, '23.492')]
[2023-02-23 09:24:08,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3762.8). Total num frames: 3428352. Throughput: 0: 918.3. Samples: 858020. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:24:08,990][00405] Avg episode reward: [(0, '23.288')]
[2023-02-23 09:24:12,409][26325] Updated weights for policy 0, policy_version 840 (0.0038)
[2023-02-23 09:24:13,989][00405] Fps is (10 sec: 3276.3, 60 sec: 3754.6, 300 sec: 3735.0). Total num frames: 3444736. Throughput: 0: 917.2. Samples: 860124. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:24:13,994][00405] Avg episode reward: [(0, '24.277')]
[2023-02-23 09:24:13,997][26306] Saving new best policy, reward=24.277!
[2023-02-23 09:24:18,988][00405] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3748.9). Total num frames: 3469312. Throughput: 0: 968.4. Samples: 867158. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:24:18,990][00405] Avg episode reward: [(0, '23.161')]
[2023-02-23 09:24:19,005][26306] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000847_3469312.pth...
[2023-02-23 09:24:19,190][26306] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000628_2572288.pth
[2023-02-23 09:24:21,184][26325] Updated weights for policy 0, policy_version 850 (0.0021)
[2023-02-23 09:24:23,987][00405] Fps is (10 sec: 4506.3, 60 sec: 3755.3, 300 sec: 3776.7). Total num frames: 3489792. Throughput: 0: 950.2. Samples: 873276. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:24:23,990][00405] Avg episode reward: [(0, '22.486')]
[2023-02-23 09:24:28,987][00405] Fps is (10 sec: 3686.5, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3506176. Throughput: 0: 919.3. Samples: 875482. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0)
[2023-02-23 09:24:28,992][00405] Avg episode reward: [(0, '22.041')]
[2023-02-23 09:24:33,617][26325] Updated weights for policy 0, policy_version 860 (0.0046)
[2023-02-23 09:24:33,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3748.9). Total num frames: 3522560. Throughput: 0: 930.4. Samples: 880300. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:24:33,993][00405] Avg episode reward: [(0, '22.212')]
[2023-02-23 09:24:38,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3547136. Throughput: 0: 972.1. Samples: 887344. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:24:38,993][00405] Avg episode reward: [(0, '20.609')]
[2023-02-23 09:24:42,734][26325] Updated weights for policy 0, policy_version 870 (0.0014)
[2023-02-23 09:24:43,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3823.3, 300 sec: 3776.7). Total num frames: 3567616. Throughput: 0: 971.1. Samples: 890852. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:24:43,995][00405] Avg episode reward: [(0, '20.965')]
[2023-02-23 09:24:48,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3579904. Throughput: 0: 924.1. Samples: 895286. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:24:48,990][00405] Avg episode reward: [(0, '20.644')]
[2023-02-23 09:24:53,987][00405] Fps is (10 sec: 3276.7, 60 sec: 3822.9, 300 sec: 3748.9). Total num frames: 3600384. Throughput: 0: 948.8. Samples: 900716. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0)
[2023-02-23 09:24:53,994][00405] Avg episode reward: [(0, '21.348')]
[2023-02-23 09:24:54,777][26325] Updated weights for policy 0, policy_version 880 (0.0038)
[2023-02-23 09:24:58,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3748.9). Total num frames: 3620864. Throughput: 0: 982.2. Samples: 904322. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:24:58,990][00405] Avg episode reward: [(0, '21.629')]
[2023-02-23 09:25:03,987][00405] Fps is (10 sec: 4096.1, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3641344. Throughput: 0: 971.2. Samples: 910862. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:25:03,998][00405] Avg episode reward: [(0, '22.700')]
[2023-02-23 09:25:04,446][26325] Updated weights for policy 0, policy_version 890 (0.0013)
[2023-02-23 09:25:08,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3657728. Throughput: 0: 932.7. Samples: 915248. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:25:08,998][00405] Avg episode reward: [(0, '22.610')]
[2023-02-23 09:25:13,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3891.3, 300 sec: 3748.9). Total num frames: 3678208. Throughput: 0: 938.6. Samples: 917718. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0)
[2023-02-23 09:25:13,989][00405] Avg episode reward: [(0, '23.019')]
[2023-02-23 09:25:15,873][26325] Updated weights for policy 0, policy_version 900 (0.0017)
[2023-02-23 09:25:18,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3698688. Throughput: 0: 986.2. Samples: 924678. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:25:18,990][00405] Avg episode reward: [(0, '22.630')]
[2023-02-23 09:25:23,988][00405] Fps is (10 sec: 4095.7, 60 sec: 3822.9, 300 sec: 3776.6). Total num frames: 3719168. Throughput: 0: 963.6. Samples: 930708. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:25:23,991][00405] Avg episode reward: [(0, '22.700')]
[2023-02-23 09:25:26,212][26325] Updated weights for policy 0, policy_version 910 (0.0028)
[2023-02-23 09:25:28,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3754.7, 300 sec: 3762.8). Total num frames: 3731456. Throughput: 0: 933.6. Samples: 932866. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:25:28,990][00405] Avg episode reward: [(0, '23.174')]
[2023-02-23 09:25:33,987][00405] Fps is (10 sec: 3277.0, 60 sec: 3822.9, 300 sec: 3748.9). Total num frames: 3751936. Throughput: 0: 946.5. Samples: 937878. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:25:33,989][00405] Avg episode reward: [(0, '23.774')]
[2023-02-23 09:25:36,951][26325] Updated weights for policy 0, policy_version 920 (0.0017)
[2023-02-23 09:25:38,987][00405] Fps is (10 sec: 4505.6, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3776512. Throughput: 0: 983.4. Samples: 944970. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:25:38,989][00405] Avg episode reward: [(0, '24.808')]
[2023-02-23 09:25:38,998][26306] Saving new best policy, reward=24.808!
[2023-02-23 09:25:43,994][00405] Fps is (10 sec: 4502.6, 60 sec: 3822.5, 300 sec: 3776.6). Total num frames: 3796992. Throughput: 0: 975.8. Samples: 948240. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:25:43,997][00405] Avg episode reward: [(0, '24.506')]
[2023-02-23 09:25:48,213][26325] Updated weights for policy 0, policy_version 930 (0.0018)
[2023-02-23 09:25:48,988][00405] Fps is (10 sec: 3276.5, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3809280. Throughput: 0: 928.6. Samples: 952652. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:25:48,993][00405] Avg episode reward: [(0, '25.399')]
[2023-02-23 09:25:49,007][26306] Saving new best policy, reward=25.399!
[2023-02-23 09:25:53,987][00405] Fps is (10 sec: 3279.0, 60 sec: 3822.9, 300 sec: 3748.9). Total num frames: 3829760. Throughput: 0: 954.8. Samples: 958216. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:25:53,994][00405] Avg episode reward: [(0, '24.497')]
[2023-02-23 09:25:58,078][26325] Updated weights for policy 0, policy_version 940 (0.0012)
[2023-02-23 09:25:58,987][00405] Fps is (10 sec: 4506.0, 60 sec: 3891.2, 300 sec: 3776.7). Total num frames: 3854336. Throughput: 0: 979.4. Samples: 961792. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:25:58,990][00405] Avg episode reward: [(0, '23.663')]
[2023-02-23 09:26:03,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3776.7). Total num frames: 3870720. Throughput: 0: 964.0. Samples: 968060. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:26:03,990][00405] Avg episode reward: [(0, '22.190')]
[2023-02-23 09:26:08,987][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3887104. Throughput: 0: 930.0. Samples: 972556. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:26:08,990][00405] Avg episode reward: [(0, '21.283')]
[2023-02-23 09:26:09,821][26325] Updated weights for policy 0, policy_version 950 (0.0038)
[2023-02-23 09:26:13,987][00405] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3907584. Throughput: 0: 939.0. Samples: 975122. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:26:13,990][00405] Avg episode reward: [(0, '21.498')]
[2023-02-23 09:26:18,987][00405] Fps is (10 sec: 4096.0, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3928064. Throughput: 0: 984.6. Samples: 982184. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0)
[2023-02-23 09:26:18,995][00405] Avg episode reward: [(0, '20.035')]
[2023-02-23 09:26:19,008][26306] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000960_3932160.pth...
[2023-02-23 09:26:19,019][26325] Updated weights for policy 0, policy_version 960 (0.0013)
[2023-02-23 09:26:19,130][26306] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000738_3022848.pth
[2023-02-23 09:26:23,989][00405] Fps is (10 sec: 4095.2, 60 sec: 3822.8, 300 sec: 3776.6). Total num frames: 3948544. Throughput: 0: 954.8. Samples: 987940. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0)
[2023-02-23 09:26:23,991][00405] Avg episode reward: [(0, '21.518')]
[2023-02-23 09:26:28,988][00405] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3762.8). Total num frames: 3960832. Throughput: 0: 930.3. Samples: 990098. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:26:28,996][00405] Avg episode reward: [(0, '22.306')]
[2023-02-23 09:26:31,647][26325] Updated weights for policy 0, policy_version 970 (0.0020)
[2023-02-23 09:26:33,987][00405] Fps is (10 sec: 3277.5, 60 sec: 3822.9, 300 sec: 3748.9). Total num frames: 3981312. Throughput: 0: 947.4. Samples: 995286. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0)
[2023-02-23 09:26:33,989][00405] Avg episode reward: [(0, '23.202')]
[2023-02-23 09:26:38,679][26306] Stopping Batcher_0...
[2023-02-23 09:26:38,679][26306] Loop batcher_evt_loop terminating...
[2023-02-23 09:26:38,681][26306] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
[2023-02-23 09:26:38,679][00405] Component Batcher_0 stopped!
[2023-02-23 09:26:38,733][26326] Stopping RolloutWorker_w0...
[2023-02-23 09:26:38,734][00405] Component RolloutWorker_w0 stopped!
[2023-02-23 09:26:38,745][26329] Stopping RolloutWorker_w3...
[2023-02-23 09:26:38,744][00405] Component RolloutWorker_w6 stopped!
[2023-02-23 09:26:38,747][00405] Component RolloutWorker_w3 stopped!
[2023-02-23 09:26:38,744][26331] Stopping RolloutWorker_w6...
[2023-02-23 09:26:38,758][26329] Loop rollout_proc3_evt_loop terminating...
[2023-02-23 09:26:38,761][00405] Component RolloutWorker_w2 stopped!
[2023-02-23 09:26:38,767][00405] Component RolloutWorker_w4 stopped!
[2023-02-23 09:26:38,767][26328] Stopping RolloutWorker_w4...
[2023-02-23 09:26:38,774][26325] Weights refcount: 2 0
[2023-02-23 09:26:38,776][26330] Stopping RolloutWorker_w5...
[2023-02-23 09:26:38,775][00405] Component RolloutWorker_w5 stopped!
[2023-02-23 09:26:38,761][26327] Stopping RolloutWorker_w2...
[2023-02-23 09:26:38,779][26332] Stopping RolloutWorker_w7...
[2023-02-23 09:26:38,779][00405] Component RolloutWorker_w7 stopped!
[2023-02-23 09:26:38,741][26326] Loop rollout_proc0_evt_loop terminating...
[2023-02-23 09:26:38,785][26332] Loop rollout_proc7_evt_loop terminating...
[2023-02-23 09:26:38,756][26331] Loop rollout_proc6_evt_loop terminating...
[2023-02-23 09:26:38,769][26328] Loop rollout_proc4_evt_loop terminating...
[2023-02-23 09:26:38,790][26325] Stopping InferenceWorker_p0-w0...
[2023-02-23 09:26:38,790][00405] Component InferenceWorker_p0-w0 stopped!
[2023-02-23 09:26:38,779][26327] Loop rollout_proc2_evt_loop terminating...
[2023-02-23 09:26:38,792][26325] Loop inference_proc0-0_evt_loop terminating...
[2023-02-23 09:26:38,799][26324] Stopping RolloutWorker_w1...
[2023-02-23 09:26:38,799][00405] Component RolloutWorker_w1 stopped!
[2023-02-23 09:26:38,777][26330] Loop rollout_proc5_evt_loop terminating...
[2023-02-23 09:26:38,810][26324] Loop rollout_proc1_evt_loop terminating...
[2023-02-23 09:26:38,876][26306] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000847_3469312.pth
[2023-02-23 09:26:38,894][26306] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
[2023-02-23 09:26:39,085][26306] Stopping LearnerWorker_p0...
[2023-02-23 09:26:39,085][26306] Loop learner_proc0_evt_loop terminating...
[2023-02-23 09:26:39,084][00405] Component LearnerWorker_p0 stopped!
[2023-02-23 09:26:39,087][00405] Waiting for process learner_proc0 to stop...
[2023-02-23 09:26:40,839][00405] Waiting for process inference_proc0-0 to join...
[2023-02-23 09:26:41,337][00405] Waiting for process rollout_proc0 to join...
[2023-02-23 09:26:41,495][00405] Waiting for process rollout_proc1 to join...
[2023-02-23 09:26:41,910][00405] Waiting for process rollout_proc2 to join...
[2023-02-23 09:26:41,912][00405] Waiting for process rollout_proc3 to join...
[2023-02-23 09:26:41,915][00405] Waiting for process rollout_proc4 to join...
[2023-02-23 09:26:41,916][00405] Waiting for process rollout_proc5 to join...
[2023-02-23 09:26:41,917][00405] Waiting for process rollout_proc6 to join...
[2023-02-23 09:26:41,918][00405] Waiting for process rollout_proc7 to join...
[2023-02-23 09:26:41,919][00405] Batcher 0 profile tree view:
batching: 25.7502, releasing_batches: 0.0256
[2023-02-23 09:26:41,923][00405] InferenceWorker_p0-w0 profile tree view:
wait_policy: 0.0000
  wait_policy_total: 524.3111
update_model: 7.5501
  weight_update: 0.0027
one_step: 0.0137
  handle_policy_step: 501.5741
    deserialize: 14.4447, stack: 2.9217, obs_to_device_normalize: 111.9713, forward: 241.5045, send_messages: 25.4338
    prepare_outputs: 80.0421
      to_cpu: 49.5330
[2023-02-23 09:26:41,924][00405] Learner 0 profile tree view:
misc: 0.0066, prepare_batch: 15.3893
train: 74.9640
  epoch_init: 0.0055, minibatch_init: 0.0111, losses_postprocess: 0.6269, kl_divergence: 0.5383, after_optimizer: 32.8731
  calculate_losses: 26.6304
    losses_init: 0.0035, forward_head: 1.7332, bptt_initial: 17.6642, tail: 1.0506, advantages_returns: 0.2445, losses: 3.5286
    bptt: 2.1444
      bptt_forward_core: 2.0186
  update: 13.7303
    clip: 1.4316
[2023-02-23 09:26:41,925][00405] RolloutWorker_w0 profile tree view:
wait_for_trajectories: 0.3370, enqueue_policy_requests: 140.0819, env_step: 806.5715, overhead: 19.9072, complete_rollouts: 6.7045
save_policy_outputs: 20.0089
  split_output_tensors: 9.9444
[2023-02-23 09:26:41,926][00405] RolloutWorker_w7 profile tree view:
wait_for_trajectories: 0.3272, enqueue_policy_requests: 139.6455, env_step: 807.3444, overhead: 20.1231, complete_rollouts: 6.6882
save_policy_outputs: 19.3572
  split_output_tensors: 9.7380
[2023-02-23 09:26:41,930][00405] Loop Runner_EvtLoop terminating...
[2023-02-23 09:26:41,931][00405] Runner profile tree view:
main_loop: 1098.0395
[2023-02-23 09:26:41,934][00405] Collected {0: 4005888}, FPS: 3648.2
[2023-02-23 09:26:52,713][00405] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json
[2023-02-23 09:26:52,717][00405] Overriding arg 'num_workers' with value 1 passed from command line
[2023-02-23 09:26:52,721][00405] Adding new argument 'no_render'=True that is not in the saved config file!
[2023-02-23 09:26:52,725][00405] Adding new argument 'save_video'=True that is not in the saved config file!
[2023-02-23 09:26:52,727][00405] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
[2023-02-23 09:26:52,729][00405] Adding new argument 'video_name'=None that is not in the saved config file!
[2023-02-23 09:26:52,732][00405] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file!
[2023-02-23 09:26:52,734][00405] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
[2023-02-23 09:26:52,737][00405] Adding new argument 'push_to_hub'=False that is not in the saved config file!
[2023-02-23 09:26:52,739][00405] Adding new argument 'hf_repository'=None that is not in the saved config file!
[2023-02-23 09:26:52,742][00405] Adding new argument 'policy_index'=0 that is not in the saved config file!
[2023-02-23 09:26:52,743][00405] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
[2023-02-23 09:26:52,745][00405] Adding new argument 'train_script'=None that is not in the saved config file!
[2023-02-23 09:26:52,747][00405] Adding new argument 'enjoy_script'=None that is not in the saved config file!
[2023-02-23 09:26:52,753][00405] Using frameskip 1 and render_action_repeat=4 for evaluation
[2023-02-23 09:26:52,775][00405] Doom resolution: 160x120, resize resolution: (128, 72)
[2023-02-23 09:26:52,779][00405] RunningMeanStd input shape: (3, 72, 128)
[2023-02-23 09:26:52,783][00405] RunningMeanStd input shape: (1,)
[2023-02-23 09:26:52,799][00405] ConvEncoder: input_channels=3
[2023-02-23 09:26:53,485][00405] Conv encoder output size: 512
[2023-02-23 09:26:53,488][00405] Policy head output size: 512
[2023-02-23 09:26:56,509][00405] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
[2023-02-23 09:26:58,192][00405] Num frames 100...
[2023-02-23 09:26:58,302][00405] Num frames 200...
[2023-02-23 09:26:58,412][00405] Num frames 300...
[2023-02-23 09:26:58,524][00405] Num frames 400...
[2023-02-23 09:26:58,639][00405] Num frames 500...
[2023-02-23 09:26:58,747][00405] Num frames 600...
[2023-02-23 09:26:58,854][00405] Num frames 700...
[2023-02-23 09:26:58,966][00405] Num frames 800...
[2023-02-23 09:26:59,080][00405] Num frames 900...
[2023-02-23 09:26:59,206][00405] Num frames 1000...
[2023-02-23 09:26:59,320][00405] Num frames 1100...
[2023-02-23 09:26:59,435][00405] Num frames 1200...
[2023-02-23 09:26:59,546][00405] Num frames 1300...
[2023-02-23 09:26:59,659][00405] Num frames 1400...
[2023-02-23 09:26:59,769][00405] Num frames 1500...
[2023-02-23 09:26:59,883][00405] Num frames 1600...
[2023-02-23 09:26:59,993][00405] Num frames 1700...
[2023-02-23 09:27:00,109][00405] Num frames 1800...
[2023-02-23 09:27:00,225][00405] Num frames 1900...
[2023-02-23 09:27:00,304][00405] Avg episode rewards: #0: 46.199, true rewards: #0: 19.200
[2023-02-23 09:27:00,305][00405] Avg episode reward: 46.199, avg true_objective: 19.200
[2023-02-23 09:27:00,399][00405] Num frames 2000...
[2023-02-23 09:27:00,515][00405] Num frames 2100...
[2023-02-23 09:27:00,629][00405] Num frames 2200...
[2023-02-23 09:27:00,743][00405] Num frames 2300...
[2023-02-23 09:27:00,852][00405] Num frames 2400...
[2023-02-23 09:27:00,966][00405] Num frames 2500...
[2023-02-23 09:27:01,088][00405] Num frames 2600...
[2023-02-23 09:27:01,212][00405] Num frames 2700...
[2023-02-23 09:27:01,328][00405] Num frames 2800...
[2023-02-23 09:27:01,443][00405] Num frames 2900...
[2023-02-23 09:27:01,567][00405] Num frames 3000...
[2023-02-23 09:27:01,683][00405] Num frames 3100...
[2023-02-23 09:27:01,795][00405] Num frames 3200...
[2023-02-23 09:27:01,911][00405] Num frames 3300...
[2023-02-23 09:27:02,039][00405] Avg episode rewards: #0: 40.315, true rewards: #0: 16.815
[2023-02-23 09:27:02,040][00405] Avg episode reward: 40.315, avg true_objective: 16.815
[2023-02-23 09:27:02,085][00405] Num frames 3400...
[2023-02-23 09:27:02,228][00405] Num frames 3500...
[2023-02-23 09:27:02,351][00405] Num frames 3600...
[2023-02-23 09:27:02,466][00405] Num frames 3700...
[2023-02-23 09:27:02,577][00405] Num frames 3800...
[2023-02-23 09:27:02,690][00405] Num frames 3900...
[2023-02-23 09:27:02,801][00405] Num frames 4000...
[2023-02-23 09:27:02,914][00405] Num frames 4100...
[2023-02-23 09:27:03,024][00405] Num frames 4200...
[2023-02-23 09:27:03,136][00405] Num frames 4300...
[2023-02-23 09:27:03,264][00405] Num frames 4400...
[2023-02-23 09:27:03,354][00405] Avg episode rewards: #0: 34.100, true rewards: #0: 14.767
[2023-02-23 09:27:03,356][00405] Avg episode reward: 34.100, avg true_objective: 14.767
[2023-02-23 09:27:03,438][00405] Num frames 4500...
[2023-02-23 09:27:03,549][00405] Num frames 4600...
[2023-02-23 09:27:03,660][00405] Num frames 4700...
[2023-02-23 09:27:03,773][00405] Num frames 4800...
[2023-02-23 09:27:03,888][00405] Num frames 4900...
[2023-02-23 09:27:04,002][00405] Num frames 5000...
[2023-02-23 09:27:04,113][00405] Num frames 5100...
[2023-02-23 09:27:04,234][00405] Num frames 5200...
[2023-02-23 09:27:04,345][00405] Num frames 5300...
[2023-02-23 09:27:04,466][00405] Num frames 5400...
[2023-02-23 09:27:04,583][00405] Avg episode rewards: #0: 31.385, true rewards: #0: 13.635
[2023-02-23 09:27:04,585][00405] Avg episode reward: 31.385, avg true_objective: 13.635
[2023-02-23 09:27:04,639][00405] Num frames 5500...
[2023-02-23 09:27:04,748][00405] Num frames 5600...
[2023-02-23 09:27:04,858][00405] Num frames 5700...
[2023-02-23 09:27:04,971][00405] Num frames 5800...
[2023-02-23 09:27:05,084][00405] Num frames 5900...
[2023-02-23 09:27:05,214][00405] Num frames 6000...
[2023-02-23 09:27:05,355][00405] Avg episode rewards: #0: 27.540, true rewards: #0: 12.140
[2023-02-23 09:27:05,358][00405] Avg episode reward: 27.540, avg true_objective: 12.140
[2023-02-23 09:27:05,399][00405] Num frames 6100...
[2023-02-23 09:27:05,516][00405] Num frames 6200...
[2023-02-23 09:27:05,626][00405] Num frames 6300...
[2023-02-23 09:27:05,739][00405] Num frames 6400...
[2023-02-23 09:27:05,850][00405] Num frames 6500...
[2023-02-23 09:27:05,926][00405] Avg episode rewards: #0: 23.863, true rewards: #0: 10.863
[2023-02-23 09:27:05,928][00405] Avg episode reward: 23.863, avg true_objective: 10.863
[2023-02-23 09:27:06,028][00405] Num frames 6600...
[2023-02-23 09:27:06,141][00405] Num frames 6700...
[2023-02-23 09:27:06,253][00405] Num frames 6800...
[2023-02-23 09:27:06,386][00405] Num frames 6900...
[2023-02-23 09:27:06,496][00405] Num frames 7000...
[2023-02-23 09:27:06,607][00405] Num frames 7100...
[2023-02-23 09:27:06,717][00405] Num frames 7200...
[2023-02-23 09:27:06,830][00405] Num frames 7300...
[2023-02-23 09:27:06,947][00405] Num frames 7400...
[2023-02-23 09:27:07,060][00405] Num frames 7500...
[2023-02-23 09:27:07,179][00405] Num frames 7600...
[2023-02-23 09:27:07,300][00405] Num frames 7700...
[2023-02-23 09:27:07,416][00405] Num frames 7800...
[2023-02-23 09:27:07,531][00405] Num frames 7900...
[2023-02-23 09:27:07,645][00405] Num frames 8000...
[2023-02-23 09:27:07,759][00405] Num frames 8100...
[2023-02-23 09:27:07,873][00405] Num frames 8200...
[2023-02-23 09:27:07,939][00405] Avg episode rewards: #0: 27.010, true rewards: #0: 11.724
[2023-02-23 09:27:07,941][00405] Avg episode reward: 27.010, avg true_objective: 11.724
[2023-02-23 09:27:08,087][00405] Num frames 8300...
[2023-02-23 09:27:08,248][00405] Num frames 8400...
[2023-02-23 09:27:08,408][00405] Num frames 8500...
[2023-02-23 09:27:08,563][00405] Num frames 8600...
[2023-02-23 09:27:08,725][00405] Num frames 8700...
[2023-02-23 09:27:08,876][00405] Num frames 8800...
[2023-02-23 09:27:09,040][00405] Num frames 8900...
[2023-02-23 09:27:09,205][00405] Num frames 9000...
[2023-02-23 09:27:09,381][00405] Num frames 9100...
[2023-02-23 09:27:09,534][00405] Num frames 9200...
[2023-02-23 09:27:09,693][00405] Num frames 9300...
[2023-02-23 09:27:09,860][00405] Num frames 9400...
[2023-02-23 09:27:10,028][00405] Num frames 9500...
[2023-02-23 09:27:10,183][00405] Num frames 9600...
[2023-02-23 09:27:10,341][00405] Num frames 9700...
[2023-02-23 09:27:10,503][00405] Num frames 9800...
[2023-02-23 09:27:10,664][00405] Num frames 9900...
[2023-02-23 09:27:10,823][00405] Num frames 10000...
[2023-02-23 09:27:10,906][00405] Avg episode rewards: #0: 29.520, true rewards: #0: 12.520
[2023-02-23 09:27:10,909][00405] Avg episode reward: 29.520, avg true_objective: 12.520
[2023-02-23 09:27:11,044][00405] Num frames 10100...
[2023-02-23 09:27:11,206][00405] Num frames 10200...
[2023-02-23 09:27:11,364][00405] Num frames 10300...
[2023-02-23 09:27:11,542][00405] Num frames 10400...
[2023-02-23 09:27:11,674][00405] Num frames 10500...
[2023-02-23 09:27:11,787][00405] Num frames 10600...
[2023-02-23 09:27:11,902][00405] Num frames 10700...
[2023-02-23 09:27:12,016][00405] Num frames 10800...
[2023-02-23 09:27:12,132][00405] Num frames 10900...
[2023-02-23 09:27:12,242][00405] Num frames 11000...
[2023-02-23 09:27:12,354][00405] Num frames 11100...
[2023-02-23 09:27:12,463][00405] Avg episode rewards: #0: 28.830, true rewards: #0: 12.386
[2023-02-23 09:27:12,465][00405] Avg episode reward: 28.830, avg true_objective: 12.386
[2023-02-23 09:27:12,526][00405] Num frames 11200...
[2023-02-23 09:27:12,635][00405] Num frames 11300...
[2023-02-23 09:27:12,746][00405] Num frames 11400...
[2023-02-23 09:27:12,862][00405] Num frames 11500...
[2023-02-23 09:27:12,974][00405] Num frames 11600...
[2023-02-23 09:27:13,089][00405] Num frames 11700...
[2023-02-23 09:27:13,205][00405] Avg episode rewards: #0: 26.855, true rewards: #0: 11.755
[2023-02-23 09:27:13,207][00405] Avg episode reward: 26.855, avg true_objective: 11.755
[2023-02-23 09:28:24,817][00405] Replay video saved to /content/train_dir/default_experiment/replay.mp4!
[2023-02-23 09:31:39,938][00405] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json
[2023-02-23 09:31:39,943][00405] Overriding arg 'num_workers' with value 1 passed from command line
[2023-02-23 09:31:39,945][00405] Adding new argument 'no_render'=True that is not in the saved config file!
[2023-02-23 09:31:39,948][00405] Adding new argument 'save_video'=True that is not in the saved config file!
[2023-02-23 09:31:39,950][00405] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
[2023-02-23 09:31:39,954][00405] Adding new argument 'video_name'=None that is not in the saved config file!
[2023-02-23 09:31:39,956][00405] Adding new argument 'max_num_frames'=100000 that is not in the saved config file!
[2023-02-23 09:31:39,957][00405] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
[2023-02-23 09:31:39,958][00405] Adding new argument 'push_to_hub'=True that is not in the saved config file!
[2023-02-23 09:31:39,959][00405] Adding new argument 'hf_repository'='Nishant91/rl_course_vizdoom_health_gathering_supreme' that is not in the saved config file!
[2023-02-23 09:31:39,960][00405] Adding new argument 'policy_index'=0 that is not in the saved config file!
[2023-02-23 09:31:39,963][00405] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
[2023-02-23 09:31:39,964][00405] Adding new argument 'train_script'=None that is not in the saved config file!
[2023-02-23 09:31:39,965][00405] Adding new argument 'enjoy_script'=None that is not in the saved config file!
[2023-02-23 09:31:39,968][00405] Using frameskip 1 and render_action_repeat=4 for evaluation
[2023-02-23 09:31:40,008][00405] RunningMeanStd input shape: (3, 72, 128)
[2023-02-23 09:31:40,011][00405] RunningMeanStd input shape: (1,)
[2023-02-23 09:31:40,032][00405] ConvEncoder: input_channels=3
[2023-02-23 09:31:40,091][00405] Conv encoder output size: 512
[2023-02-23 09:31:40,093][00405] Policy head output size: 512
[2023-02-23 09:31:40,121][00405] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
[2023-02-23 09:31:40,788][00405] Num frames 100...
[2023-02-23 09:31:40,949][00405] Num frames 200...
[2023-02-23 09:31:41,120][00405] Num frames 300...
[2023-02-23 09:31:41,288][00405] Num frames 400...
[2023-02-23 09:31:41,457][00405] Num frames 500...
[2023-02-23 09:31:41,620][00405] Num frames 600...
[2023-02-23 09:31:41,780][00405] Num frames 700...
[2023-02-23 09:31:41,941][00405] Num frames 800...
[2023-02-23 09:31:42,105][00405] Num frames 900...
[2023-02-23 09:31:42,262][00405] Num frames 1000...
[2023-02-23 09:31:42,422][00405] Num frames 1100...
[2023-02-23 09:31:42,539][00405] Num frames 1200...
[2023-02-23 09:31:42,654][00405] Num frames 1300...
[2023-02-23 09:31:42,768][00405] Num frames 1400...
[2023-02-23 09:31:42,834][00405] Avg episode rewards: #0: 31.080, true rewards: #0: 14.080
[2023-02-23 09:31:42,835][00405] Avg episode reward: 31.080, avg true_objective: 14.080
[2023-02-23 09:31:42,941][00405] Num frames 1500...
[2023-02-23 09:31:43,057][00405] Num frames 1600...
[2023-02-23 09:31:43,182][00405] Num frames 1700...
[2023-02-23 09:31:43,293][00405] Num frames 1800...
[2023-02-23 09:31:43,411][00405] Num frames 1900...
[2023-02-23 09:31:43,522][00405] Num frames 2000...
[2023-02-23 09:31:43,633][00405] Num frames 2100...
[2023-02-23 09:31:43,744][00405] Num frames 2200...
[2023-02-23 09:31:43,875][00405] Num frames 2300...
[2023-02-23 09:31:43,987][00405] Num frames 2400...
[2023-02-23 09:31:44,121][00405] Avg episode rewards: #0: 26.840, true rewards: #0: 12.340
[2023-02-23 09:31:44,123][00405] Avg episode reward: 26.840, avg true_objective: 12.340
[2023-02-23 09:31:44,162][00405] Num frames 2500...
[2023-02-23 09:31:44,276][00405] Num frames 2600...
[2023-02-23 09:31:44,388][00405] Num frames 2700...
[2023-02-23 09:31:44,499][00405] Num frames 2800...
[2023-02-23 09:31:44,611][00405] Num frames 2900...
[2023-02-23 09:31:44,722][00405] Num frames 3000...
[2023-02-23 09:31:44,849][00405] Num frames 3100...
[2023-02-23 09:31:44,965][00405] Num frames 3200...
[2023-02-23 09:31:45,082][00405] Num frames 3300...
[2023-02-23 09:31:45,202][00405] Num frames 3400...
[2023-02-23 09:31:45,325][00405] Num frames 3500...
[2023-02-23 09:31:45,440][00405] Num frames 3600...
[2023-02-23 09:31:45,555][00405] Num frames 3700...
[2023-02-23 09:31:45,673][00405] Num frames 3800...
[2023-02-23 09:31:45,786][00405] Num frames 3900...
[2023-02-23 09:31:45,901][00405] Num frames 4000...
[2023-02-23 09:31:46,012][00405] Num frames 4100...
[2023-02-23 09:31:46,142][00405] Num frames 4200...
[2023-02-23 09:31:46,260][00405] Num frames 4300...
[2023-02-23 09:31:46,343][00405] Avg episode rewards: #0: 33.413, true rewards: #0: 14.413
[2023-02-23 09:31:46,345][00405] Avg episode reward: 33.413, avg true_objective: 14.413
[2023-02-23 09:31:46,435][00405] Num frames 4400...
[2023-02-23 09:31:46,550][00405] Num frames 4500...
[2023-02-23 09:31:46,663][00405] Num frames 4600...
[2023-02-23 09:31:46,780][00405] Num frames 4700...
[2023-02-23 09:31:46,899][00405] Num frames 4800...
[2023-02-23 09:31:47,015][00405] Num frames 4900...
[2023-02-23 09:31:47,145][00405] Num frames 5000...
[2023-02-23 09:31:47,265][00405] Num frames 5100...
[2023-02-23 09:31:47,390][00405] Num frames 5200...
[2023-02-23 09:31:47,504][00405] Num frames 5300...
[2023-02-23 09:31:47,621][00405] Num frames 5400...
[2023-02-23 09:31:47,734][00405] Num frames 5500...
[2023-02-23 09:31:47,846][00405] Num frames 5600...
[2023-02-23 09:31:47,961][00405] Num frames 5700...
[2023-02-23 09:31:48,073][00405] Num frames 5800...
[2023-02-23 09:31:48,194][00405] Num frames 5900...
[2023-02-23 09:31:48,316][00405] Num frames 6000...
[2023-02-23 09:31:48,427][00405] Num frames 6100...
[2023-02-23 09:31:48,544][00405] Num frames 6200...
[2023-02-23 09:31:48,660][00405] Num frames 6300...
[2023-02-23 09:31:48,726][00405] Avg episode rewards: #0: 37.770, true rewards: #0: 15.770
[2023-02-23 09:31:48,727][00405] Avg episode reward: 37.770, avg true_objective: 15.770
[2023-02-23 09:31:48,834][00405] Num frames 6400...
[2023-02-23 09:31:48,952][00405] Num frames 6500...
[2023-02-23 09:31:49,065][00405] Num frames 6600...
[2023-02-23 09:31:49,183][00405] Num frames 6700...
[2023-02-23 09:31:49,307][00405] Num frames 6800...
[2023-02-23 09:31:49,422][00405] Num frames 6900...
[2023-02-23 09:31:49,535][00405] Num frames 7000...
[2023-02-23 09:31:49,648][00405] Num frames 7100...
[2023-02-23 09:31:49,759][00405] Num frames 7200...
[2023-02-23 09:31:49,870][00405] Num frames 7300...
[2023-02-23 09:31:49,985][00405] Num frames 7400...
[2023-02-23 09:31:50,098][00405] Num frames 7500...
[2023-02-23 09:31:50,231][00405] Num frames 7600...
[2023-02-23 09:31:50,382][00405] Avg episode rewards: #0: 36.550, true rewards: #0: 15.350
[2023-02-23 09:31:50,385][00405] Avg episode reward: 36.550, avg true_objective: 15.350
[2023-02-23 09:31:50,414][00405] Num frames 7700...
[2023-02-23 09:31:50,524][00405] Num frames 7800...
[2023-02-23 09:31:50,639][00405] Num frames 7900...
[2023-02-23 09:31:50,771][00405] Num frames 8000...
[2023-02-23 09:31:50,901][00405] Num frames 8100...
[2023-02-23 09:31:51,018][00405] Num frames 8200...
[2023-02-23 09:31:51,134][00405] Num frames 8300...
[2023-02-23 09:31:51,262][00405] Num frames 8400...
[2023-02-23 09:31:51,375][00405] Num frames 8500...
[2023-02-23 09:31:51,490][00405] Num frames 8600...
[2023-02-23 09:31:51,609][00405] Num frames 8700...
[2023-02-23 09:31:51,701][00405] Avg episode rewards: #0: 34.385, true rewards: #0: 14.552
[2023-02-23 09:31:51,704][00405] Avg episode reward: 34.385, avg true_objective: 14.552
[2023-02-23 09:31:51,785][00405] Num frames 8800...
[2023-02-23 09:31:51,898][00405] Num frames 8900...
[2023-02-23 09:31:52,014][00405] Num frames 9000...
[2023-02-23 09:31:52,125][00405] Num frames 9100...
[2023-02-23 09:31:52,251][00405] Num frames 9200...
[2023-02-23 09:31:52,373][00405] Num frames 9300...
[2023-02-23 09:31:52,514][00405] Num frames 9400...
[2023-02-23 09:31:52,682][00405] Num frames 9500...
[2023-02-23 09:31:52,879][00405] Avg episode rewards: #0: 32.126, true rewards: #0: 13.697
[2023-02-23 09:31:52,882][00405] Avg episode reward: 32.126, avg true_objective: 13.697
[2023-02-23 09:31:52,908][00405] Num frames 9600...
[2023-02-23 09:31:53,065][00405] Num frames 9700...
[2023-02-23 09:31:53,232][00405] Num frames 9800...
[2023-02-23 09:31:53,400][00405] Num frames 9900...
[2023-02-23 09:31:53,559][00405] Num frames 10000...
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[2023-02-23 09:31:53,889][00405] Num frames 10200...
[2023-02-23 09:31:54,049][00405] Num frames 10300...
[2023-02-23 09:31:54,207][00405] Num frames 10400...
[2023-02-23 09:31:54,374][00405] Num frames 10500...
[2023-02-23 09:31:54,562][00405] Avg episode rewards: #0: 31.100, true rewards: #0: 13.225
[2023-02-23 09:31:54,564][00405] Avg episode reward: 31.100, avg true_objective: 13.225
[2023-02-23 09:31:54,602][00405] Num frames 10600...
[2023-02-23 09:31:54,771][00405] Num frames 10700...
[2023-02-23 09:31:54,934][00405] Num frames 10800...
[2023-02-23 09:31:55,101][00405] Num frames 10900...
[2023-02-23 09:31:55,264][00405] Num frames 11000...
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[2023-02-23 09:31:56,257][00405] Num frames 11700...
[2023-02-23 09:31:56,378][00405] Num frames 11800...
[2023-02-23 09:31:56,539][00405] Avg episode rewards: #0: 30.769, true rewards: #0: 13.213
[2023-02-23 09:31:56,541][00405] Avg episode reward: 30.769, avg true_objective: 13.213
[2023-02-23 09:31:56,555][00405] Num frames 11900...
[2023-02-23 09:31:56,667][00405] Num frames 12000...
[2023-02-23 09:31:56,783][00405] Num frames 12100...
[2023-02-23 09:31:56,898][00405] Num frames 12200...
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[2023-02-23 09:31:57,129][00405] Num frames 12400...
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[2023-02-23 09:31:57,368][00405] Num frames 12600...
[2023-02-23 09:31:57,477][00405] Num frames 12700...
[2023-02-23 09:31:57,592][00405] Num frames 12800...
[2023-02-23 09:31:57,709][00405] Num frames 12900...
[2023-02-23 09:31:57,824][00405] Num frames 13000...
[2023-02-23 09:31:57,971][00405] Avg episode rewards: #0: 30.476, true rewards: #0: 13.076
[2023-02-23 09:31:57,973][00405] Avg episode reward: 30.476, avg true_objective: 13.076
[2023-02-23 09:33:15,545][00405] Replay video saved to /content/train_dir/default_experiment/replay.mp4!
[2023-02-23 09:34:16,396][00405] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json
[2023-02-23 09:34:16,406][00405] Overriding arg 'num_workers' with value 1 passed from command line
[2023-02-23 09:34:16,409][00405] Adding new argument 'no_render'=True that is not in the saved config file!
[2023-02-23 09:34:16,413][00405] Adding new argument 'save_video'=True that is not in the saved config file!
[2023-02-23 09:34:16,416][00405] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!
[2023-02-23 09:34:16,417][00405] Adding new argument 'video_name'=None that is not in the saved config file!
[2023-02-23 09:34:16,420][00405] Adding new argument 'max_num_frames'=100000 that is not in the saved config file!
[2023-02-23 09:34:16,423][00405] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!
[2023-02-23 09:34:16,425][00405] Adding new argument 'push_to_hub'=True that is not in the saved config file!
[2023-02-23 09:34:16,427][00405] Adding new argument 'hf_repository'='Nishant91/rl_course_vizdoom_health_gathering_supreme' that is not in the saved config file!
[2023-02-23 09:34:16,430][00405] Adding new argument 'policy_index'=0 that is not in the saved config file!
[2023-02-23 09:34:16,432][00405] Adding new argument 'eval_deterministic'=False that is not in the saved config file!
[2023-02-23 09:34:16,435][00405] Adding new argument 'train_script'=None that is not in the saved config file!
[2023-02-23 09:34:16,438][00405] Adding new argument 'enjoy_script'=None that is not in the saved config file!
[2023-02-23 09:34:16,442][00405] Using frameskip 1 and render_action_repeat=4 for evaluation
[2023-02-23 09:34:16,508][00405] RunningMeanStd input shape: (3, 72, 128)
[2023-02-23 09:34:16,516][00405] RunningMeanStd input shape: (1,)
[2023-02-23 09:34:16,552][00405] ConvEncoder: input_channels=3
[2023-02-23 09:34:16,633][00405] Conv encoder output size: 512
[2023-02-23 09:34:16,635][00405] Policy head output size: 512
[2023-02-23 09:34:16,671][00405] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth...
[2023-02-23 09:34:17,384][00405] Num frames 100...
[2023-02-23 09:34:17,501][00405] Num frames 200...
[2023-02-23 09:34:17,612][00405] Num frames 300...
[2023-02-23 09:34:17,728][00405] Num frames 400...
[2023-02-23 09:34:17,850][00405] Num frames 500...
[2023-02-23 09:34:17,976][00405] Avg episode rewards: #0: 9.620, true rewards: #0: 5.620
[2023-02-23 09:34:17,981][00405] Avg episode reward: 9.620, avg true_objective: 5.620
[2023-02-23 09:34:18,025][00405] Num frames 600...
[2023-02-23 09:34:18,137][00405] Num frames 700...
[2023-02-23 09:34:18,249][00405] Num frames 800...
[2023-02-23 09:34:18,377][00405] Num frames 900...
[2023-02-23 09:34:18,485][00405] Avg episode rewards: #0: 6.730, true rewards: #0: 4.730
[2023-02-23 09:34:18,487][00405] Avg episode reward: 6.730, avg true_objective: 4.730
[2023-02-23 09:34:18,552][00405] Num frames 1000...
[2023-02-23 09:34:18,668][00405] Num frames 1100...
[2023-02-23 09:34:18,796][00405] Num frames 1200...
[2023-02-23 09:34:18,914][00405] Num frames 1300...
[2023-02-23 09:34:19,030][00405] Num frames 1400...
[2023-02-23 09:34:19,144][00405] Num frames 1500...
[2023-02-23 09:34:19,281][00405] Num frames 1600...
[2023-02-23 09:34:19,458][00405] Num frames 1700...
[2023-02-23 09:34:19,537][00405] Avg episode rewards: #0: 10.047, true rewards: #0: 5.713
[2023-02-23 09:34:19,541][00405] Avg episode reward: 10.047, avg true_objective: 5.713
[2023-02-23 09:34:19,684][00405] Num frames 1800...
[2023-02-23 09:34:19,846][00405] Num frames 1900...
[2023-02-23 09:34:20,007][00405] Num frames 2000...
[2023-02-23 09:34:20,175][00405] Num frames 2100...
[2023-02-23 09:34:20,340][00405] Num frames 2200...
[2023-02-23 09:34:20,509][00405] Num frames 2300...
[2023-02-23 09:34:20,666][00405] Num frames 2400...
[2023-02-23 09:34:20,824][00405] Num frames 2500...
[2023-02-23 09:34:20,979][00405] Num frames 2600...
[2023-02-23 09:34:21,132][00405] Num frames 2700...
[2023-02-23 09:34:21,292][00405] Num frames 2800...
[2023-02-23 09:34:21,355][00405] Avg episode rewards: #0: 13.255, true rewards: #0: 7.005
[2023-02-23 09:34:21,357][00405] Avg episode reward: 13.255, avg true_objective: 7.005
[2023-02-23 09:34:21,522][00405] Num frames 2900...
[2023-02-23 09:34:21,682][00405] Num frames 3000...
[2023-02-23 09:34:21,842][00405] Num frames 3100...
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[2023-02-23 09:34:22,510][00405] Num frames 3500...
[2023-02-23 09:34:22,628][00405] Avg episode rewards: #0: 13.266, true rewards: #0: 7.066
[2023-02-23 09:34:22,631][00405] Avg episode reward: 13.266, avg true_objective: 7.066
[2023-02-23 09:34:22,748][00405] Num frames 3600...
[2023-02-23 09:34:22,893][00405] Num frames 3700...
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[2023-02-23 09:34:23,238][00405] Num frames 4000...
[2023-02-23 09:34:23,362][00405] Num frames 4100...
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[2023-02-23 09:34:23,613][00405] Num frames 4300...
[2023-02-23 09:34:23,738][00405] Num frames 4400...
[2023-02-23 09:34:23,848][00405] Num frames 4500...
[2023-02-23 09:34:23,959][00405] Num frames 4600...
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[2023-02-23 09:34:24,434][00405] Num frames 5000...
[2023-02-23 09:34:24,552][00405] Num frames 5100...
[2023-02-23 09:34:24,610][00405] Avg episode rewards: #0: 17.168, true rewards: #0: 8.502
[2023-02-23 09:34:24,612][00405] Avg episode reward: 17.168, avg true_objective: 8.502
[2023-02-23 09:34:24,724][00405] Num frames 5200...
[2023-02-23 09:34:24,837][00405] Num frames 5300...
[2023-02-23 09:34:24,948][00405] Num frames 5400...
[2023-02-23 09:34:25,066][00405] Num frames 5500...
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[2023-02-23 09:34:25,512][00405] Num frames 5900...
[2023-02-23 09:34:25,635][00405] Num frames 6000...
[2023-02-23 09:34:25,753][00405] Num frames 6100...
[2023-02-23 09:34:25,864][00405] Num frames 6200...
[2023-02-23 09:34:25,974][00405] Num frames 6300...
[2023-02-23 09:34:26,104][00405] Avg episode rewards: #0: 19.232, true rewards: #0: 9.089
[2023-02-23 09:34:26,106][00405] Avg episode reward: 19.232, avg true_objective: 9.089
[2023-02-23 09:34:26,152][00405] Num frames 6400...
[2023-02-23 09:34:26,262][00405] Num frames 6500...
[2023-02-23 09:34:26,383][00405] Num frames 6600...
[2023-02-23 09:34:26,496][00405] Num frames 6700...
[2023-02-23 09:34:26,613][00405] Num frames 6800...
[2023-02-23 09:34:26,724][00405] Num frames 6900...
[2023-02-23 09:34:26,833][00405] Num frames 7000...
[2023-02-23 09:34:26,945][00405] Num frames 7100...
[2023-02-23 09:34:27,062][00405] Num frames 7200...
[2023-02-23 09:34:27,175][00405] Num frames 7300...
[2023-02-23 09:34:27,296][00405] Num frames 7400...
[2023-02-23 09:34:27,410][00405] Num frames 7500...
[2023-02-23 09:34:27,525][00405] Num frames 7600...
[2023-02-23 09:34:27,648][00405] Num frames 7700...
[2023-02-23 09:34:27,760][00405] Num frames 7800...
[2023-02-23 09:34:27,872][00405] Num frames 7900...
[2023-02-23 09:34:27,992][00405] Num frames 8000...
[2023-02-23 09:34:28,105][00405] Num frames 8100...
[2023-02-23 09:34:28,219][00405] Avg episode rewards: #0: 22.065, true rewards: #0: 10.190
[2023-02-23 09:34:28,222][00405] Avg episode reward: 22.065, avg true_objective: 10.190
[2023-02-23 09:34:28,286][00405] Num frames 8200...
[2023-02-23 09:34:28,402][00405] Num frames 8300...
[2023-02-23 09:34:28,517][00405] Num frames 8400...
[2023-02-23 09:34:28,642][00405] Num frames 8500...
[2023-02-23 09:34:28,753][00405] Num frames 8600...
[2023-02-23 09:34:28,864][00405] Num frames 8700...
[2023-02-23 09:34:28,983][00405] Num frames 8800...
[2023-02-23 09:34:29,096][00405] Num frames 8900...
[2023-02-23 09:34:29,224][00405] Num frames 9000...
[2023-02-23 09:34:29,336][00405] Avg episode rewards: #0: 21.387, true rewards: #0: 10.053
[2023-02-23 09:34:29,337][00405] Avg episode reward: 21.387, avg true_objective: 10.053
[2023-02-23 09:34:29,400][00405] Num frames 9100...
[2023-02-23 09:34:29,516][00405] Num frames 9200...
[2023-02-23 09:34:29,642][00405] Num frames 9300...
[2023-02-23 09:34:29,761][00405] Num frames 9400...
[2023-02-23 09:34:29,873][00405] Num frames 9500...
[2023-02-23 09:34:29,991][00405] Num frames 9600...
[2023-02-23 09:34:30,104][00405] Num frames 9700...
[2023-02-23 09:34:30,223][00405] Num frames 9800...
[2023-02-23 09:34:30,397][00405] Avg episode rewards: #0: 20.994, true rewards: #0: 9.894
[2023-02-23 09:34:30,399][00405] Avg episode reward: 20.994, avg true_objective: 9.894
[2023-02-23 09:35:29,457][00405] Replay video saved to /content/train_dir/default_experiment/replay.mp4!