diff --git "a/sf_log.txt" "b/sf_log.txt" new file mode 100644--- /dev/null +++ "b/sf_log.txt" @@ -0,0 +1,1100 @@ +[2023-02-24 13:37:48,414][00980] Saving configuration to /content/train_dir/default_experiment/config.json... +[2023-02-24 13:37:48,419][00980] Rollout worker 0 uses device cpu +[2023-02-24 13:37:48,421][00980] Rollout worker 1 uses device cpu +[2023-02-24 13:37:48,424][00980] Rollout worker 2 uses device cpu +[2023-02-24 13:37:48,425][00980] Rollout worker 3 uses device cpu +[2023-02-24 13:37:48,426][00980] Rollout worker 4 uses device cpu +[2023-02-24 13:37:48,427][00980] Rollout worker 5 uses device cpu +[2023-02-24 13:37:48,429][00980] Rollout worker 6 uses device cpu +[2023-02-24 13:37:48,430][00980] Rollout worker 7 uses device cpu +[2023-02-24 13:37:48,612][00980] Using GPUs [0] for process 0 (actually maps to GPUs [0]) +[2023-02-24 13:37:48,614][00980] InferenceWorker_p0-w0: min num requests: 2 +[2023-02-24 13:37:48,648][00980] Starting all processes... +[2023-02-24 13:37:48,650][00980] Starting process learner_proc0 +[2023-02-24 13:37:48,704][00980] Starting all processes... +[2023-02-24 13:37:48,713][00980] Starting process inference_proc0-0 +[2023-02-24 13:37:48,713][00980] Starting process rollout_proc0 +[2023-02-24 13:37:48,715][00980] Starting process rollout_proc1 +[2023-02-24 13:37:48,715][00980] Starting process rollout_proc2 +[2023-02-24 13:37:48,715][00980] Starting process rollout_proc3 +[2023-02-24 13:37:48,715][00980] Starting process rollout_proc4 +[2023-02-24 13:37:48,716][00980] Starting process rollout_proc5 +[2023-02-24 13:37:48,716][00980] Starting process rollout_proc6 +[2023-02-24 13:37:48,716][00980] Starting process rollout_proc7 +[2023-02-24 13:38:00,073][11152] Using GPUs [0] for process 0 (actually maps to GPUs [0]) +[2023-02-24 13:38:00,074][11152] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0 +[2023-02-24 13:38:00,181][11169] Worker 1 uses CPU cores [1] +[2023-02-24 13:38:00,225][11171] Worker 5 uses CPU cores [1] +[2023-02-24 13:38:00,643][11170] Worker 4 uses CPU cores [0] +[2023-02-24 13:38:00,644][11166] Worker 0 uses CPU cores [0] +[2023-02-24 13:38:00,645][11168] Worker 2 uses CPU cores [0] +[2023-02-24 13:38:00,656][11174] Worker 7 uses CPU cores [1] +[2023-02-24 13:38:00,681][11173] Worker 3 uses CPU cores [1] +[2023-02-24 13:38:00,723][11172] Worker 6 uses CPU cores [0] +[2023-02-24 13:38:00,808][11167] Using GPUs [0] for process 0 (actually maps to GPUs [0]) +[2023-02-24 13:38:00,809][11167] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0 +[2023-02-24 13:38:01,040][11152] Num visible devices: 1 +[2023-02-24 13:38:01,040][11167] Num visible devices: 1 +[2023-02-24 13:38:01,054][11152] Starting seed is not provided +[2023-02-24 13:38:01,054][11152] Using GPUs [0] for process 0 (actually maps to GPUs [0]) +[2023-02-24 13:38:01,055][11152] Initializing actor-critic model on device cuda:0 +[2023-02-24 13:38:01,055][11152] RunningMeanStd input shape: (3, 72, 128) +[2023-02-24 13:38:01,057][11152] RunningMeanStd input shape: (1,) +[2023-02-24 13:38:01,069][11152] ConvEncoder: input_channels=3 +[2023-02-24 13:38:01,348][11152] Conv encoder output size: 512 +[2023-02-24 13:38:01,348][11152] Policy head output size: 512 +[2023-02-24 13:38:01,398][11152] Created Actor Critic model with architecture: +[2023-02-24 13:38:01,398][11152] 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) + ) +) +[2023-02-24 13:38:08,318][11152] Using optimizer +[2023-02-24 13:38:08,319][11152] No checkpoints found +[2023-02-24 13:38:08,319][11152] Did not load from checkpoint, starting from scratch! +[2023-02-24 13:38:08,319][11152] Initialized policy 0 weights for model version 0 +[2023-02-24 13:38:08,324][11152] Using GPUs [0] for process 0 (actually maps to GPUs [0]) +[2023-02-24 13:38:08,331][11152] LearnerWorker_p0 finished initialization! +[2023-02-24 13:38:08,532][11167] RunningMeanStd input shape: (3, 72, 128) +[2023-02-24 13:38:08,533][11167] RunningMeanStd input shape: (1,) +[2023-02-24 13:38:08,545][11167] ConvEncoder: input_channels=3 +[2023-02-24 13:38:08,605][00980] Heartbeat connected on Batcher_0 +[2023-02-24 13:38:08,612][00980] Heartbeat connected on LearnerWorker_p0 +[2023-02-24 13:38:08,624][00980] Heartbeat connected on RolloutWorker_w0 +[2023-02-24 13:38:08,627][00980] Heartbeat connected on RolloutWorker_w1 +[2023-02-24 13:38:08,632][00980] Heartbeat connected on RolloutWorker_w2 +[2023-02-24 13:38:08,635][00980] Heartbeat connected on RolloutWorker_w3 +[2023-02-24 13:38:08,638][00980] Heartbeat connected on RolloutWorker_w4 +[2023-02-24 13:38:08,641][00980] Heartbeat connected on RolloutWorker_w5 +[2023-02-24 13:38:08,645][00980] Heartbeat connected on RolloutWorker_w6 +[2023-02-24 13:38:08,648][00980] Heartbeat connected on RolloutWorker_w7 +[2023-02-24 13:38:08,675][11167] Conv encoder output size: 512 +[2023-02-24 13:38:08,675][11167] Policy head output size: 512 +[2023-02-24 13:38:11,675][00980] Inference worker 0-0 is ready! +[2023-02-24 13:38:11,680][00980] All inference workers are ready! Signal rollout workers to start! +[2023-02-24 13:38:11,682][00980] Heartbeat connected on InferenceWorker_p0-w0 +[2023-02-24 13:38:11,815][11170] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 13:38:11,823][11166] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 13:38:11,836][11172] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 13:38:11,861][11168] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 13:38:11,939][11171] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 13:38:11,970][11169] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 13:38:11,949][11174] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 13:38:11,997][11173] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 13:38:12,941][00980] 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) +[2023-02-24 13:38:13,336][11171] Decorrelating experience for 0 frames... +[2023-02-24 13:38:13,337][11173] Decorrelating experience for 0 frames... +[2023-02-24 13:38:13,337][11172] Decorrelating experience for 0 frames... +[2023-02-24 13:38:13,336][11170] Decorrelating experience for 0 frames... +[2023-02-24 13:38:13,334][11166] Decorrelating experience for 0 frames... +[2023-02-24 13:38:14,535][11173] Decorrelating experience for 32 frames... +[2023-02-24 13:38:14,540][11169] Decorrelating experience for 0 frames... +[2023-02-24 13:38:14,548][11168] Decorrelating experience for 0 frames... +[2023-02-24 13:38:14,554][11171] Decorrelating experience for 32 frames... +[2023-02-24 13:38:14,564][11170] Decorrelating experience for 32 frames... +[2023-02-24 13:38:15,085][11169] Decorrelating experience for 32 frames... +[2023-02-24 13:38:15,712][11166] Decorrelating experience for 32 frames... +[2023-02-24 13:38:15,719][11168] Decorrelating experience for 32 frames... +[2023-02-24 13:38:15,721][11172] Decorrelating experience for 32 frames... +[2023-02-24 13:38:15,962][11170] Decorrelating experience for 64 frames... +[2023-02-24 13:38:16,447][11173] Decorrelating experience for 64 frames... +[2023-02-24 13:38:16,505][11174] Decorrelating experience for 0 frames... +[2023-02-24 13:38:17,213][11168] Decorrelating experience for 64 frames... +[2023-02-24 13:38:17,219][11172] Decorrelating experience for 64 frames... +[2023-02-24 13:38:17,357][11170] Decorrelating experience for 96 frames... +[2023-02-24 13:38:17,559][11171] Decorrelating experience for 64 frames... +[2023-02-24 13:38:17,646][11174] Decorrelating experience for 32 frames... +[2023-02-24 13:38:17,740][11173] Decorrelating experience for 96 frames... +[2023-02-24 13:38:17,909][11166] Decorrelating experience for 64 frames... +[2023-02-24 13:38:17,940][00980] 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) +[2023-02-24 13:38:18,591][11172] Decorrelating experience for 96 frames... +[2023-02-24 13:38:18,779][11168] Decorrelating experience for 96 frames... +[2023-02-24 13:38:19,018][11166] Decorrelating experience for 96 frames... +[2023-02-24 13:38:19,203][11171] Decorrelating experience for 96 frames... +[2023-02-24 13:38:19,352][11174] Decorrelating experience for 64 frames... +[2023-02-24 13:38:19,715][11169] Decorrelating experience for 64 frames... +[2023-02-24 13:38:20,019][11174] Decorrelating experience for 96 frames... +[2023-02-24 13:38:20,258][11169] Decorrelating experience for 96 frames... +[2023-02-24 13:38:22,940][00980] Fps is (10 sec: 0.0, 60 sec: 0.0, 300 sec: 0.0). Total num frames: 0. Throughput: 0: 4.4. Samples: 44. Policy #0 lag: (min: -1.0, avg: -1.0, max: -1.0) +[2023-02-24 13:38:22,942][00980] Avg episode reward: [(0, '1.652')] +[2023-02-24 13:38:24,162][11152] Signal inference workers to stop experience collection... +[2023-02-24 13:38:24,169][11167] InferenceWorker_p0-w0: stopping experience collection +[2023-02-24 13:38:26,901][11152] Signal inference workers to resume experience collection... +[2023-02-24 13:38:26,901][11167] InferenceWorker_p0-w0: resuming experience collection +[2023-02-24 13:38:27,940][00980] Fps is (10 sec: 409.6, 60 sec: 273.1, 300 sec: 273.1). Total num frames: 4096. Throughput: 0: 173.4. Samples: 2600. Policy #0 lag: (min: 0.0, avg: 0.0, max: 0.0) +[2023-02-24 13:38:27,943][00980] Avg episode reward: [(0, '2.516')] +[2023-02-24 13:38:32,941][00980] Fps is (10 sec: 2047.8, 60 sec: 1024.0, 300 sec: 1024.0). Total num frames: 20480. Throughput: 0: 293.0. Samples: 5860. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:38:32,943][00980] Avg episode reward: [(0, '3.416')] +[2023-02-24 13:38:36,828][11167] Updated weights for policy 0, policy_version 10 (0.0013) +[2023-02-24 13:38:37,940][00980] Fps is (10 sec: 4096.0, 60 sec: 1802.4, 300 sec: 1802.4). Total num frames: 45056. Throughput: 0: 363.7. Samples: 9092. Policy #0 lag: (min: 0.0, avg: 0.2, max: 1.0) +[2023-02-24 13:38:37,941][00980] Avg episode reward: [(0, '4.245')] +[2023-02-24 13:38:42,940][00980] Fps is (10 sec: 4506.1, 60 sec: 2184.6, 300 sec: 2184.6). Total num frames: 65536. Throughput: 0: 537.4. Samples: 16120. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:38:42,943][00980] Avg episode reward: [(0, '4.308')] +[2023-02-24 13:38:47,434][11167] Updated weights for policy 0, policy_version 20 (0.0013) +[2023-02-24 13:38:47,940][00980] Fps is (10 sec: 3686.4, 60 sec: 2340.7, 300 sec: 2340.7). Total num frames: 81920. Throughput: 0: 606.9. Samples: 21242. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 13:38:47,943][00980] Avg episode reward: [(0, '4.248')] +[2023-02-24 13:38:52,940][00980] Fps is (10 sec: 2867.2, 60 sec: 2355.3, 300 sec: 2355.3). Total num frames: 94208. Throughput: 0: 585.2. Samples: 23408. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:38:52,948][00980] Avg episode reward: [(0, '4.218')] +[2023-02-24 13:38:57,940][00980] Fps is (10 sec: 3686.4, 60 sec: 2639.7, 300 sec: 2639.7). Total num frames: 118784. Throughput: 0: 650.8. Samples: 29286. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:38:57,942][00980] Avg episode reward: [(0, '4.411')] +[2023-02-24 13:38:57,945][11152] Saving new best policy, reward=4.411! +[2023-02-24 13:38:58,493][11167] Updated weights for policy 0, policy_version 30 (0.0020) +[2023-02-24 13:39:02,940][00980] Fps is (10 sec: 4915.3, 60 sec: 2867.3, 300 sec: 2867.3). Total num frames: 143360. Throughput: 0: 807.6. Samples: 36342. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:39:02,942][00980] Avg episode reward: [(0, '4.305')] +[2023-02-24 13:39:07,940][00980] Fps is (10 sec: 4096.0, 60 sec: 2904.5, 300 sec: 2904.5). Total num frames: 159744. Throughput: 0: 864.7. Samples: 38956. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) +[2023-02-24 13:39:07,947][00980] Avg episode reward: [(0, '4.309')] +[2023-02-24 13:39:09,169][11167] Updated weights for policy 0, policy_version 40 (0.0027) +[2023-02-24 13:39:12,940][00980] Fps is (10 sec: 2867.0, 60 sec: 2867.2, 300 sec: 2867.2). Total num frames: 172032. Throughput: 0: 906.8. Samples: 43406. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:39:12,947][00980] Avg episode reward: [(0, '4.432')] +[2023-02-24 13:39:12,961][11152] Saving new best policy, reward=4.432! +[2023-02-24 13:39:17,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3276.8, 300 sec: 3024.8). Total num frames: 196608. Throughput: 0: 973.5. Samples: 49666. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:39:17,947][00980] Avg episode reward: [(0, '4.399')] +[2023-02-24 13:39:19,472][11167] Updated weights for policy 0, policy_version 50 (0.0025) +[2023-02-24 13:39:22,940][00980] Fps is (10 sec: 4915.6, 60 sec: 3686.4, 300 sec: 3159.8). Total num frames: 221184. Throughput: 0: 979.9. Samples: 53186. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:39:22,942][00980] Avg episode reward: [(0, '4.317')] +[2023-02-24 13:39:27,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.2, 300 sec: 3167.6). Total num frames: 237568. Throughput: 0: 951.6. Samples: 58944. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 13:39:27,944][00980] Avg episode reward: [(0, '4.377')] +[2023-02-24 13:39:30,688][11167] Updated weights for policy 0, policy_version 60 (0.0021) +[2023-02-24 13:39:32,940][00980] Fps is (10 sec: 2867.2, 60 sec: 3823.0, 300 sec: 3123.3). Total num frames: 249856. Throughput: 0: 938.6. Samples: 63480. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:39:32,942][00980] Avg episode reward: [(0, '4.564')] +[2023-02-24 13:39:32,961][11152] Saving new best policy, reward=4.564! +[2023-02-24 13:39:37,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3228.7). Total num frames: 274432. Throughput: 0: 960.5. Samples: 66632. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:39:37,947][00980] Avg episode reward: [(0, '4.579')] +[2023-02-24 13:39:37,952][11152] Saving new best policy, reward=4.579! +[2023-02-24 13:39:40,521][11167] Updated weights for policy 0, policy_version 70 (0.0024) +[2023-02-24 13:39:42,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3822.9, 300 sec: 3276.9). Total num frames: 294912. Throughput: 0: 985.4. Samples: 73630. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:39:42,943][00980] Avg episode reward: [(0, '4.581')] +[2023-02-24 13:39:42,956][11152] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000072_294912.pth... +[2023-02-24 13:39:43,156][11152] Saving new best policy, reward=4.581! +[2023-02-24 13:39:47,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3276.9). Total num frames: 311296. Throughput: 0: 941.9. Samples: 78726. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:39:47,945][00980] Avg episode reward: [(0, '4.659')] +[2023-02-24 13:39:47,949][11152] Saving new best policy, reward=4.659! +[2023-02-24 13:39:52,851][11167] Updated weights for policy 0, policy_version 80 (0.0015) +[2023-02-24 13:39:52,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3891.2, 300 sec: 3276.9). Total num frames: 327680. Throughput: 0: 929.0. Samples: 80760. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:39:52,942][00980] Avg episode reward: [(0, '4.729')] +[2023-02-24 13:39:52,955][11152] Saving new best policy, reward=4.729! +[2023-02-24 13:39:57,940][00980] Fps is (10 sec: 3686.3, 60 sec: 3822.9, 300 sec: 3315.9). Total num frames: 348160. Throughput: 0: 960.9. Samples: 86646. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:39:57,943][00980] Avg episode reward: [(0, '4.488')] +[2023-02-24 13:40:01,727][11167] Updated weights for policy 0, policy_version 90 (0.0021) +[2023-02-24 13:40:02,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3822.9, 300 sec: 3388.6). Total num frames: 372736. Throughput: 0: 983.8. Samples: 93938. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:40:02,943][00980] Avg episode reward: [(0, '4.355')] +[2023-02-24 13:40:07,940][00980] Fps is (10 sec: 4096.1, 60 sec: 3822.9, 300 sec: 3383.7). Total num frames: 389120. Throughput: 0: 964.3. Samples: 96578. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:40:07,945][00980] Avg episode reward: [(0, '4.497')] +[2023-02-24 13:40:12,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3891.2, 300 sec: 3379.2). Total num frames: 405504. Throughput: 0: 936.4. Samples: 101084. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:40:12,943][00980] Avg episode reward: [(0, '4.596')] +[2023-02-24 13:40:13,930][11167] Updated weights for policy 0, policy_version 100 (0.0047) +[2023-02-24 13:40:17,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3407.9). Total num frames: 425984. Throughput: 0: 977.8. Samples: 107482. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:40:17,942][00980] Avg episode reward: [(0, '4.536')] +[2023-02-24 13:40:22,536][11167] Updated weights for policy 0, policy_version 110 (0.0022) +[2023-02-24 13:40:22,940][00980] Fps is (10 sec: 4505.5, 60 sec: 3822.9, 300 sec: 3465.9). Total num frames: 450560. Throughput: 0: 987.2. Samples: 111054. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:40:22,943][00980] Avg episode reward: [(0, '4.550')] +[2023-02-24 13:40:27,942][00980] Fps is (10 sec: 4094.9, 60 sec: 3822.8, 300 sec: 3458.8). Total num frames: 466944. Throughput: 0: 956.5. Samples: 116674. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:40:27,945][00980] Avg episode reward: [(0, '4.564')] +[2023-02-24 13:40:32,940][00980] Fps is (10 sec: 2867.3, 60 sec: 3822.9, 300 sec: 3423.1). Total num frames: 479232. Throughput: 0: 943.0. Samples: 121160. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 13:40:32,948][00980] Avg episode reward: [(0, '4.622')] +[2023-02-24 13:40:34,819][11167] Updated weights for policy 0, policy_version 120 (0.0035) +[2023-02-24 13:40:37,940][00980] Fps is (10 sec: 3687.3, 60 sec: 3822.9, 300 sec: 3474.6). Total num frames: 503808. Throughput: 0: 970.1. Samples: 124414. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:40:37,952][00980] Avg episode reward: [(0, '4.742')] +[2023-02-24 13:40:37,960][11152] Saving new best policy, reward=4.742! +[2023-02-24 13:40:42,940][00980] Fps is (10 sec: 4915.1, 60 sec: 3891.2, 300 sec: 3522.6). Total num frames: 528384. Throughput: 0: 993.2. Samples: 131340. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:40:42,943][00980] Avg episode reward: [(0, '4.836')] +[2023-02-24 13:40:42,954][11152] Saving new best policy, reward=4.836! +[2023-02-24 13:40:43,915][11167] Updated weights for policy 0, policy_version 130 (0.0016) +[2023-02-24 13:40:47,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3488.2). Total num frames: 540672. Throughput: 0: 945.4. Samples: 136480. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 13:40:47,942][00980] Avg episode reward: [(0, '4.832')] +[2023-02-24 13:40:52,941][00980] Fps is (10 sec: 2867.0, 60 sec: 3822.9, 300 sec: 3481.6). Total num frames: 557056. Throughput: 0: 934.8. Samples: 138646. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:40:52,947][00980] Avg episode reward: [(0, '4.762')] +[2023-02-24 13:40:56,054][11167] Updated weights for policy 0, policy_version 140 (0.0025) +[2023-02-24 13:40:57,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.2, 300 sec: 3525.1). Total num frames: 581632. Throughput: 0: 970.1. Samples: 144738. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:40:57,946][00980] Avg episode reward: [(0, '4.819')] +[2023-02-24 13:41:02,942][00980] Fps is (10 sec: 4914.4, 60 sec: 3891.0, 300 sec: 3565.9). Total num frames: 606208. Throughput: 0: 987.2. Samples: 151908. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:41:02,947][00980] Avg episode reward: [(0, '4.724')] +[2023-02-24 13:41:05,336][11167] Updated weights for policy 0, policy_version 150 (0.0016) +[2023-02-24 13:41:07,943][00980] Fps is (10 sec: 3685.0, 60 sec: 3822.7, 300 sec: 3534.2). Total num frames: 618496. Throughput: 0: 961.7. Samples: 154332. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:41:07,949][00980] Avg episode reward: [(0, '4.639')] +[2023-02-24 13:41:12,943][00980] Fps is (10 sec: 2866.9, 60 sec: 3822.7, 300 sec: 3527.1). Total num frames: 634880. Throughput: 0: 935.8. Samples: 158786. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:41:12,946][00980] Avg episode reward: [(0, '4.888')] +[2023-02-24 13:41:12,966][11152] Saving new best policy, reward=4.888! +[2023-02-24 13:41:17,053][11167] Updated weights for policy 0, policy_version 160 (0.0031) +[2023-02-24 13:41:17,940][00980] Fps is (10 sec: 4097.5, 60 sec: 3891.2, 300 sec: 3564.7). Total num frames: 659456. Throughput: 0: 979.3. Samples: 165230. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:41:17,945][00980] Avg episode reward: [(0, '4.879')] +[2023-02-24 13:41:22,940][00980] Fps is (10 sec: 4507.2, 60 sec: 3822.9, 300 sec: 3578.6). Total num frames: 679936. Throughput: 0: 984.0. Samples: 168692. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:41:22,942][00980] Avg episode reward: [(0, '4.645')] +[2023-02-24 13:41:27,308][11167] Updated weights for policy 0, policy_version 170 (0.0025) +[2023-02-24 13:41:27,945][00980] Fps is (10 sec: 3684.5, 60 sec: 3822.8, 300 sec: 3570.8). Total num frames: 696320. Throughput: 0: 952.9. Samples: 174226. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:41:27,948][00980] Avg episode reward: [(0, '4.523')] +[2023-02-24 13:41:32,941][00980] Fps is (10 sec: 3276.3, 60 sec: 3891.1, 300 sec: 3563.5). Total num frames: 712704. Throughput: 0: 939.6. Samples: 178764. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:41:32,949][00980] Avg episode reward: [(0, '4.561')] +[2023-02-24 13:41:37,940][00980] Fps is (10 sec: 3688.3, 60 sec: 3822.9, 300 sec: 3576.5). Total num frames: 733184. Throughput: 0: 967.9. Samples: 182202. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:41:37,942][00980] Avg episode reward: [(0, '4.777')] +[2023-02-24 13:41:37,966][11167] Updated weights for policy 0, policy_version 180 (0.0012) +[2023-02-24 13:41:42,940][00980] Fps is (10 sec: 4506.3, 60 sec: 3822.9, 300 sec: 3608.4). Total num frames: 757760. Throughput: 0: 991.1. Samples: 189336. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:41:42,946][00980] Avg episode reward: [(0, '4.800')] +[2023-02-24 13:41:42,957][11152] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000185_757760.pth... +[2023-02-24 13:41:47,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.2, 300 sec: 3600.7). Total num frames: 774144. Throughput: 0: 944.4. Samples: 194404. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:41:47,943][00980] Avg episode reward: [(0, '4.725')] +[2023-02-24 13:41:48,509][11167] Updated weights for policy 0, policy_version 190 (0.0022) +[2023-02-24 13:41:52,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3891.3, 300 sec: 3593.3). Total num frames: 790528. Throughput: 0: 941.7. Samples: 196704. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 13:41:52,945][00980] Avg episode reward: [(0, '4.819')] +[2023-02-24 13:41:57,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.2, 300 sec: 3622.7). Total num frames: 815104. Throughput: 0: 978.3. Samples: 202806. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 13:41:57,945][00980] Avg episode reward: [(0, '5.099')] +[2023-02-24 13:41:57,949][11152] Saving new best policy, reward=5.099! +[2023-02-24 13:41:58,857][11167] Updated weights for policy 0, policy_version 200 (0.0017) +[2023-02-24 13:42:02,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3823.1, 300 sec: 3633.0). Total num frames: 835584. Throughput: 0: 993.7. Samples: 209946. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 13:42:02,946][00980] Avg episode reward: [(0, '5.284')] +[2023-02-24 13:42:02,959][11152] Saving new best policy, reward=5.284! +[2023-02-24 13:42:07,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3891.4, 300 sec: 3625.4). Total num frames: 851968. Throughput: 0: 972.4. Samples: 212452. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:42:07,947][00980] Avg episode reward: [(0, '5.709')] +[2023-02-24 13:42:07,949][11152] Saving new best policy, reward=5.709! +[2023-02-24 13:42:10,052][11167] Updated weights for policy 0, policy_version 210 (0.0013) +[2023-02-24 13:42:12,941][00980] Fps is (10 sec: 3276.4, 60 sec: 3891.3, 300 sec: 3618.1). Total num frames: 868352. Throughput: 0: 948.8. Samples: 216918. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:42:12,944][00980] Avg episode reward: [(0, '5.637')] +[2023-02-24 13:42:17,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3627.9). Total num frames: 888832. Throughput: 0: 995.5. Samples: 223560. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:42:17,953][00980] Avg episode reward: [(0, '5.676')] +[2023-02-24 13:42:19,687][11167] Updated weights for policy 0, policy_version 220 (0.0018) +[2023-02-24 13:42:22,940][00980] Fps is (10 sec: 4506.1, 60 sec: 3891.2, 300 sec: 3653.7). Total num frames: 913408. Throughput: 0: 998.4. Samples: 227128. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 13:42:22,944][00980] Avg episode reward: [(0, '5.737')] +[2023-02-24 13:42:23,034][11152] Saving new best policy, reward=5.737! +[2023-02-24 13:42:27,942][00980] Fps is (10 sec: 4095.1, 60 sec: 3891.4, 300 sec: 3646.2). Total num frames: 929792. Throughput: 0: 966.9. Samples: 232850. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:42:27,952][00980] Avg episode reward: [(0, '5.743')] +[2023-02-24 13:42:27,960][11152] Saving new best policy, reward=5.743! +[2023-02-24 13:42:31,215][11167] Updated weights for policy 0, policy_version 230 (0.0014) +[2023-02-24 13:42:32,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3891.3, 300 sec: 3639.2). Total num frames: 946176. Throughput: 0: 952.4. Samples: 237264. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:42:32,944][00980] Avg episode reward: [(0, '5.787')] +[2023-02-24 13:42:32,954][11152] Saving new best policy, reward=5.787! +[2023-02-24 13:42:37,940][00980] Fps is (10 sec: 4096.9, 60 sec: 3959.5, 300 sec: 3663.2). Total num frames: 970752. Throughput: 0: 978.3. Samples: 240728. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 13:42:37,942][00980] Avg episode reward: [(0, '6.137')] +[2023-02-24 13:42:37,948][11152] Saving new best policy, reward=6.137! +[2023-02-24 13:42:40,345][11167] Updated weights for policy 0, policy_version 240 (0.0017) +[2023-02-24 13:42:42,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3891.2, 300 sec: 3671.3). Total num frames: 991232. Throughput: 0: 999.2. Samples: 247770. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 13:42:42,945][00980] Avg episode reward: [(0, '6.197')] +[2023-02-24 13:42:42,962][11152] Saving new best policy, reward=6.197! +[2023-02-24 13:42:47,942][00980] Fps is (10 sec: 3685.6, 60 sec: 3891.1, 300 sec: 3664.1). Total num frames: 1007616. Throughput: 0: 953.6. Samples: 252862. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:42:47,946][00980] Avg episode reward: [(0, '6.539')] +[2023-02-24 13:42:47,948][11152] Saving new best policy, reward=6.539! +[2023-02-24 13:42:52,614][11167] Updated weights for policy 0, policy_version 250 (0.0030) +[2023-02-24 13:42:52,942][00980] Fps is (10 sec: 3276.2, 60 sec: 3891.1, 300 sec: 3657.1). Total num frames: 1024000. Throughput: 0: 945.6. Samples: 255006. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:42:52,946][00980] Avg episode reward: [(0, '6.493')] +[2023-02-24 13:42:57,940][00980] Fps is (10 sec: 3687.2, 60 sec: 3822.9, 300 sec: 3664.9). Total num frames: 1044480. Throughput: 0: 984.6. Samples: 261222. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:42:57,942][00980] Avg episode reward: [(0, '6.429')] +[2023-02-24 13:43:01,309][11167] Updated weights for policy 0, policy_version 260 (0.0016) +[2023-02-24 13:43:02,940][00980] Fps is (10 sec: 4506.4, 60 sec: 3891.2, 300 sec: 3686.4). Total num frames: 1069056. Throughput: 0: 998.5. Samples: 268492. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:43:02,943][00980] Avg episode reward: [(0, '6.526')] +[2023-02-24 13:43:07,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.2, 300 sec: 3679.5). Total num frames: 1085440. Throughput: 0: 974.4. Samples: 270974. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:43:07,948][00980] Avg episode reward: [(0, '6.938')] +[2023-02-24 13:43:08,030][11152] Saving new best policy, reward=6.938! +[2023-02-24 13:43:12,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3891.3, 300 sec: 3735.0). Total num frames: 1101824. Throughput: 0: 947.2. Samples: 275470. Policy #0 lag: (min: 0.0, avg: 0.3, max: 2.0) +[2023-02-24 13:43:12,948][00980] Avg episode reward: [(0, '7.288')] +[2023-02-24 13:43:12,964][11152] Saving new best policy, reward=7.288! +[2023-02-24 13:43:13,547][11167] Updated weights for policy 0, policy_version 270 (0.0019) +[2023-02-24 13:43:17,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3959.5, 300 sec: 3818.3). Total num frames: 1126400. Throughput: 0: 991.3. Samples: 281872. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 13:43:17,945][00980] Avg episode reward: [(0, '6.738')] +[2023-02-24 13:43:22,174][11167] Updated weights for policy 0, policy_version 280 (0.0025) +[2023-02-24 13:43:22,940][00980] Fps is (10 sec: 4505.5, 60 sec: 3891.2, 300 sec: 3873.8). Total num frames: 1146880. Throughput: 0: 992.3. Samples: 285380. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) +[2023-02-24 13:43:22,943][00980] Avg episode reward: [(0, '6.764')] +[2023-02-24 13:43:27,940][00980] Fps is (10 sec: 3686.3, 60 sec: 3891.3, 300 sec: 3873.9). Total num frames: 1163264. Throughput: 0: 963.4. Samples: 291122. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 13:43:27,950][00980] Avg episode reward: [(0, '7.105')] +[2023-02-24 13:43:32,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3891.2, 300 sec: 3846.1). Total num frames: 1179648. Throughput: 0: 950.9. Samples: 295652. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:43:32,946][00980] Avg episode reward: [(0, '7.147')] +[2023-02-24 13:43:34,461][11167] Updated weights for policy 0, policy_version 290 (0.0019) +[2023-02-24 13:43:37,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.2, 300 sec: 3860.0). Total num frames: 1204224. Throughput: 0: 978.1. Samples: 299018. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:43:37,945][00980] Avg episode reward: [(0, '7.403')] +[2023-02-24 13:43:37,951][11152] Saving new best policy, reward=7.403! +[2023-02-24 13:43:42,940][00980] Fps is (10 sec: 4505.7, 60 sec: 3891.2, 300 sec: 3873.8). Total num frames: 1224704. Throughput: 0: 996.7. Samples: 306074. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:43:42,944][00980] Avg episode reward: [(0, '8.056')] +[2023-02-24 13:43:42,953][11152] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000299_1224704.pth... +[2023-02-24 13:43:43,101][11152] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000072_294912.pth +[2023-02-24 13:43:43,110][11152] Saving new best policy, reward=8.056! +[2023-02-24 13:43:43,489][11167] Updated weights for policy 0, policy_version 300 (0.0023) +[2023-02-24 13:43:47,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3891.3, 300 sec: 3887.7). Total num frames: 1241088. Throughput: 0: 947.8. Samples: 311142. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:43:47,945][00980] Avg episode reward: [(0, '8.824')] +[2023-02-24 13:43:47,951][11152] Saving new best policy, reward=8.824! +[2023-02-24 13:43:52,940][00980] Fps is (10 sec: 3276.6, 60 sec: 3891.3, 300 sec: 3860.0). Total num frames: 1257472. Throughput: 0: 939.8. Samples: 313264. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:43:52,943][00980] Avg episode reward: [(0, '9.508')] +[2023-02-24 13:43:52,971][11152] Saving new best policy, reward=9.508! +[2023-02-24 13:43:55,690][11167] Updated weights for policy 0, policy_version 310 (0.0024) +[2023-02-24 13:43:57,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3891.2, 300 sec: 3846.1). Total num frames: 1277952. Throughput: 0: 970.8. Samples: 319158. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:43:57,945][00980] Avg episode reward: [(0, '9.624')] +[2023-02-24 13:43:57,947][11152] Saving new best policy, reward=9.624! +[2023-02-24 13:44:02,940][00980] Fps is (10 sec: 4505.9, 60 sec: 3891.2, 300 sec: 3873.8). Total num frames: 1302528. Throughput: 0: 985.2. Samples: 326206. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:44:02,945][00980] Avg episode reward: [(0, '8.869')] +[2023-02-24 13:44:04,905][11167] Updated weights for policy 0, policy_version 320 (0.0023) +[2023-02-24 13:44:07,940][00980] Fps is (10 sec: 4095.9, 60 sec: 3891.2, 300 sec: 3887.7). Total num frames: 1318912. Throughput: 0: 964.5. Samples: 328784. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 13:44:07,945][00980] Avg episode reward: [(0, '8.736')] +[2023-02-24 13:44:12,941][00980] Fps is (10 sec: 3276.5, 60 sec: 3891.1, 300 sec: 3859.9). Total num frames: 1335296. Throughput: 0: 937.4. Samples: 333304. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 13:44:12,946][00980] Avg episode reward: [(0, '9.513')] +[2023-02-24 13:44:16,504][11167] Updated weights for policy 0, policy_version 330 (0.0021) +[2023-02-24 13:44:17,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3846.1). Total num frames: 1355776. Throughput: 0: 982.5. Samples: 339864. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:44:17,948][00980] Avg episode reward: [(0, '9.925')] +[2023-02-24 13:44:17,953][11152] Saving new best policy, reward=9.925! +[2023-02-24 13:44:22,940][00980] Fps is (10 sec: 4506.1, 60 sec: 3891.2, 300 sec: 3873.8). Total num frames: 1380352. Throughput: 0: 985.2. Samples: 343354. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 13:44:22,943][00980] Avg episode reward: [(0, '10.988')] +[2023-02-24 13:44:22,954][11152] Saving new best policy, reward=10.988! +[2023-02-24 13:44:26,333][11167] Updated weights for policy 0, policy_version 340 (0.0014) +[2023-02-24 13:44:27,940][00980] Fps is (10 sec: 4096.1, 60 sec: 3891.2, 300 sec: 3887.7). Total num frames: 1396736. Throughput: 0: 953.3. Samples: 348974. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:44:27,945][00980] Avg episode reward: [(0, '10.246')] +[2023-02-24 13:44:32,940][00980] Fps is (10 sec: 2867.2, 60 sec: 3822.9, 300 sec: 3846.1). Total num frames: 1409024. Throughput: 0: 943.7. Samples: 353608. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:44:32,949][00980] Avg episode reward: [(0, '10.671')] +[2023-02-24 13:44:37,313][11167] Updated weights for policy 0, policy_version 350 (0.0030) +[2023-02-24 13:44:37,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3822.9, 300 sec: 3860.0). Total num frames: 1433600. Throughput: 0: 970.6. Samples: 356940. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:44:37,942][00980] Avg episode reward: [(0, '10.539')] +[2023-02-24 13:44:42,940][00980] Fps is (10 sec: 4915.1, 60 sec: 3891.2, 300 sec: 3887.7). Total num frames: 1458176. Throughput: 0: 998.4. Samples: 364086. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:44:42,943][00980] Avg episode reward: [(0, '10.770')] +[2023-02-24 13:44:47,461][11167] Updated weights for policy 0, policy_version 360 (0.0028) +[2023-02-24 13:44:47,941][00980] Fps is (10 sec: 4095.3, 60 sec: 3891.1, 300 sec: 3887.7). Total num frames: 1474560. Throughput: 0: 959.2. Samples: 369370. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:44:47,951][00980] Avg episode reward: [(0, '11.574')] +[2023-02-24 13:44:47,959][11152] Saving new best policy, reward=11.574! +[2023-02-24 13:44:52,940][00980] Fps is (10 sec: 2867.3, 60 sec: 3823.0, 300 sec: 3860.0). Total num frames: 1486848. Throughput: 0: 949.8. Samples: 371524. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:44:52,945][00980] Avg episode reward: [(0, '12.029')] +[2023-02-24 13:44:52,963][11152] Saving new best policy, reward=12.029! +[2023-02-24 13:44:57,940][00980] Fps is (10 sec: 3687.0, 60 sec: 3891.2, 300 sec: 3860.0). Total num frames: 1511424. Throughput: 0: 982.5. Samples: 377514. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:44:57,946][00980] Avg episode reward: [(0, '13.549')] +[2023-02-24 13:44:57,950][11152] Saving new best policy, reward=13.549! +[2023-02-24 13:44:58,343][11167] Updated weights for policy 0, policy_version 370 (0.0021) +[2023-02-24 13:45:02,940][00980] Fps is (10 sec: 4915.2, 60 sec: 3891.2, 300 sec: 3887.7). Total num frames: 1536000. Throughput: 0: 996.8. Samples: 384718. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:45:02,942][00980] Avg episode reward: [(0, '13.641')] +[2023-02-24 13:45:02,955][11152] Saving new best policy, reward=13.641! +[2023-02-24 13:45:07,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.2, 300 sec: 3887.7). Total num frames: 1552384. Throughput: 0: 976.7. Samples: 387304. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 13:45:07,943][00980] Avg episode reward: [(0, '13.450')] +[2023-02-24 13:45:08,858][11167] Updated weights for policy 0, policy_version 380 (0.0019) +[2023-02-24 13:45:12,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3891.3, 300 sec: 3873.8). Total num frames: 1568768. Throughput: 0: 953.3. Samples: 391874. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:45:12,945][00980] Avg episode reward: [(0, '12.302')] +[2023-02-24 13:45:17,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3891.2, 300 sec: 3860.0). Total num frames: 1589248. Throughput: 0: 993.2. Samples: 398302. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:45:17,942][00980] Avg episode reward: [(0, '11.242')] +[2023-02-24 13:45:18,981][11167] Updated weights for policy 0, policy_version 390 (0.0014) +[2023-02-24 13:45:22,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3891.2, 300 sec: 3887.8). Total num frames: 1613824. Throughput: 0: 999.1. Samples: 401898. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:45:22,942][00980] Avg episode reward: [(0, '11.683')] +[2023-02-24 13:45:27,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.2, 300 sec: 3901.6). Total num frames: 1630208. Throughput: 0: 970.1. Samples: 407740. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:45:27,948][00980] Avg episode reward: [(0, '11.982')] +[2023-02-24 13:45:29,929][11167] Updated weights for policy 0, policy_version 400 (0.0024) +[2023-02-24 13:45:32,940][00980] Fps is (10 sec: 3276.7, 60 sec: 3959.5, 300 sec: 3873.8). Total num frames: 1646592. Throughput: 0: 951.9. Samples: 412202. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:45:32,948][00980] Avg episode reward: [(0, '12.382')] +[2023-02-24 13:45:37,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3822.9, 300 sec: 3846.1). Total num frames: 1662976. Throughput: 0: 962.6. Samples: 414840. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:45:37,948][00980] Avg episode reward: [(0, '13.828')] +[2023-02-24 13:45:37,950][11152] Saving new best policy, reward=13.828! +[2023-02-24 13:45:41,821][11167] Updated weights for policy 0, policy_version 410 (0.0015) +[2023-02-24 13:45:42,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3873.8). Total num frames: 1683456. Throughput: 0: 950.4. Samples: 420280. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:45:42,943][00980] Avg episode reward: [(0, '13.286')] +[2023-02-24 13:45:42,953][11152] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000411_1683456.pth... +[2023-02-24 13:45:43,087][11152] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000185_757760.pth +[2023-02-24 13:45:47,941][00980] Fps is (10 sec: 3686.1, 60 sec: 3754.7, 300 sec: 3873.8). Total num frames: 1699840. Throughput: 0: 908.7. Samples: 425610. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:45:47,950][00980] Avg episode reward: [(0, '13.446')] +[2023-02-24 13:45:52,940][00980] Fps is (10 sec: 2867.2, 60 sec: 3754.7, 300 sec: 3832.2). Total num frames: 1712128. Throughput: 0: 900.2. Samples: 427814. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:45:52,949][00980] Avg episode reward: [(0, '14.849')] +[2023-02-24 13:45:52,959][11152] Saving new best policy, reward=14.849! +[2023-02-24 13:45:54,263][11167] Updated weights for policy 0, policy_version 420 (0.0045) +[2023-02-24 13:45:57,940][00980] Fps is (10 sec: 3686.7, 60 sec: 3754.7, 300 sec: 3832.2). Total num frames: 1736704. Throughput: 0: 928.5. Samples: 433658. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:45:57,948][00980] Avg episode reward: [(0, '15.688')] +[2023-02-24 13:45:57,952][11152] Saving new best policy, reward=15.688! +[2023-02-24 13:46:02,793][11167] Updated weights for policy 0, policy_version 430 (0.0017) +[2023-02-24 13:46:02,940][00980] Fps is (10 sec: 4915.3, 60 sec: 3754.7, 300 sec: 3873.9). Total num frames: 1761280. Throughput: 0: 943.8. Samples: 440774. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:46:02,947][00980] Avg episode reward: [(0, '15.851')] +[2023-02-24 13:46:02,961][11152] Saving new best policy, reward=15.851! +[2023-02-24 13:46:07,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3873.9). Total num frames: 1777664. Throughput: 0: 920.5. Samples: 443322. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:46:07,947][00980] Avg episode reward: [(0, '14.958')] +[2023-02-24 13:46:12,940][00980] Fps is (10 sec: 2867.2, 60 sec: 3686.4, 300 sec: 3832.2). Total num frames: 1789952. Throughput: 0: 892.1. Samples: 447886. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:46:12,945][00980] Avg episode reward: [(0, '13.428')] +[2023-02-24 13:46:15,105][11167] Updated weights for policy 0, policy_version 440 (0.0014) +[2023-02-24 13:46:17,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3754.7, 300 sec: 3846.1). Total num frames: 1814528. Throughput: 0: 934.8. Samples: 454268. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:46:17,946][00980] Avg episode reward: [(0, '13.581')] +[2023-02-24 13:46:22,940][00980] Fps is (10 sec: 4915.3, 60 sec: 3754.7, 300 sec: 3873.9). Total num frames: 1839104. Throughput: 0: 957.1. Samples: 457908. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:46:22,945][00980] Avg episode reward: [(0, '13.883')] +[2023-02-24 13:46:23,563][11167] Updated weights for policy 0, policy_version 450 (0.0012) +[2023-02-24 13:46:27,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3754.7, 300 sec: 3873.9). Total num frames: 1855488. Throughput: 0: 965.1. Samples: 463710. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:46:27,944][00980] Avg episode reward: [(0, '14.481')] +[2023-02-24 13:46:32,940][00980] Fps is (10 sec: 2867.1, 60 sec: 3686.4, 300 sec: 3846.1). Total num frames: 1867776. Throughput: 0: 944.3. Samples: 468102. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:46:32,943][00980] Avg episode reward: [(0, '14.688')] +[2023-02-24 13:46:35,998][11167] Updated weights for policy 0, policy_version 460 (0.0018) +[2023-02-24 13:46:37,940][00980] Fps is (10 sec: 3686.3, 60 sec: 3822.9, 300 sec: 3846.1). Total num frames: 1892352. Throughput: 0: 967.8. Samples: 471366. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 13:46:37,943][00980] Avg episode reward: [(0, '14.700')] +[2023-02-24 13:46:42,940][00980] Fps is (10 sec: 4915.3, 60 sec: 3891.2, 300 sec: 3873.8). Total num frames: 1916928. Throughput: 0: 999.8. Samples: 478648. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 13:46:42,942][00980] Avg episode reward: [(0, '14.915')] +[2023-02-24 13:46:44,917][11167] Updated weights for policy 0, policy_version 470 (0.0016) +[2023-02-24 13:46:47,941][00980] Fps is (10 sec: 4095.6, 60 sec: 3891.2, 300 sec: 3873.8). Total num frames: 1933312. Throughput: 0: 962.1. Samples: 484070. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 13:46:47,944][00980] Avg episode reward: [(0, '15.869')] +[2023-02-24 13:46:47,946][11152] Saving new best policy, reward=15.869! +[2023-02-24 13:46:52,940][00980] Fps is (10 sec: 2867.2, 60 sec: 3891.2, 300 sec: 3832.2). Total num frames: 1945600. Throughput: 0: 953.3. Samples: 486220. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:46:52,945][00980] Avg episode reward: [(0, '16.879')] +[2023-02-24 13:46:52,977][11152] Saving new best policy, reward=16.879! +[2023-02-24 13:46:56,655][11167] Updated weights for policy 0, policy_version 480 (0.0021) +[2023-02-24 13:46:57,940][00980] Fps is (10 sec: 3686.9, 60 sec: 3891.2, 300 sec: 3846.1). Total num frames: 1970176. Throughput: 0: 984.3. Samples: 492180. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 13:46:57,942][00980] Avg episode reward: [(0, '17.186')] +[2023-02-24 13:46:57,949][11152] Saving new best policy, reward=17.186! +[2023-02-24 13:47:02,940][00980] Fps is (10 sec: 4915.1, 60 sec: 3891.2, 300 sec: 3873.8). Total num frames: 1994752. Throughput: 0: 1001.2. Samples: 499320. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:47:02,943][00980] Avg episode reward: [(0, '17.813')] +[2023-02-24 13:47:02,953][11152] Saving new best policy, reward=17.813! +[2023-02-24 13:47:06,352][11167] Updated weights for policy 0, policy_version 490 (0.0013) +[2023-02-24 13:47:07,942][00980] Fps is (10 sec: 4095.1, 60 sec: 3891.1, 300 sec: 3873.8). Total num frames: 2011136. Throughput: 0: 976.7. Samples: 501860. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:47:07,952][00980] Avg episode reward: [(0, '18.134')] +[2023-02-24 13:47:07,954][11152] Saving new best policy, reward=18.134! +[2023-02-24 13:47:12,940][00980] Fps is (10 sec: 2867.3, 60 sec: 3891.2, 300 sec: 3846.1). Total num frames: 2023424. Throughput: 0: 947.6. Samples: 506350. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:47:12,943][00980] Avg episode reward: [(0, '17.744')] +[2023-02-24 13:47:17,484][11167] Updated weights for policy 0, policy_version 500 (0.0025) +[2023-02-24 13:47:17,940][00980] Fps is (10 sec: 3687.2, 60 sec: 3891.2, 300 sec: 3846.1). Total num frames: 2048000. Throughput: 0: 993.2. Samples: 512794. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:47:17,942][00980] Avg episode reward: [(0, '16.609')] +[2023-02-24 13:47:22,940][00980] Fps is (10 sec: 4915.2, 60 sec: 3891.2, 300 sec: 3873.9). Total num frames: 2072576. Throughput: 0: 1001.3. Samples: 516424. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:47:22,942][00980] Avg episode reward: [(0, '17.144')] +[2023-02-24 13:47:27,541][11167] Updated weights for policy 0, policy_version 510 (0.0019) +[2023-02-24 13:47:27,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.2, 300 sec: 3873.8). Total num frames: 2088960. Throughput: 0: 966.0. Samples: 522118. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:47:27,945][00980] Avg episode reward: [(0, '17.301')] +[2023-02-24 13:47:32,940][00980] Fps is (10 sec: 3276.7, 60 sec: 3959.4, 300 sec: 3846.1). Total num frames: 2105344. Throughput: 0: 947.3. Samples: 526696. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:47:32,946][00980] Avg episode reward: [(0, '17.404')] +[2023-02-24 13:47:37,890][11167] Updated weights for policy 0, policy_version 520 (0.0014) +[2023-02-24 13:47:37,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3959.5, 300 sec: 3860.0). Total num frames: 2129920. Throughput: 0: 977.2. Samples: 530192. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:47:37,950][00980] Avg episode reward: [(0, '17.412')] +[2023-02-24 13:47:42,940][00980] Fps is (10 sec: 4505.8, 60 sec: 3891.2, 300 sec: 3873.9). Total num frames: 2150400. Throughput: 0: 1006.9. Samples: 537490. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 13:47:42,942][00980] Avg episode reward: [(0, '17.494')] +[2023-02-24 13:47:42,957][11152] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000525_2150400.pth... +[2023-02-24 13:47:43,094][11152] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000299_1224704.pth +[2023-02-24 13:47:47,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3891.3, 300 sec: 3873.9). Total num frames: 2166784. Throughput: 0: 961.7. Samples: 542594. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 13:47:47,943][00980] Avg episode reward: [(0, '19.088')] +[2023-02-24 13:47:47,948][11152] Saving new best policy, reward=19.088! +[2023-02-24 13:47:48,795][11167] Updated weights for policy 0, policy_version 530 (0.0032) +[2023-02-24 13:47:52,940][00980] Fps is (10 sec: 3276.6, 60 sec: 3959.4, 300 sec: 3859.9). Total num frames: 2183168. Throughput: 0: 954.4. Samples: 544806. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:47:52,949][00980] Avg episode reward: [(0, '20.210')] +[2023-02-24 13:47:52,965][11152] Saving new best policy, reward=20.210! +[2023-02-24 13:47:57,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3959.5, 300 sec: 3860.0). Total num frames: 2207744. Throughput: 0: 991.3. Samples: 550958. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:47:57,947][00980] Avg episode reward: [(0, '20.504')] +[2023-02-24 13:47:57,951][11152] Saving new best policy, reward=20.504! +[2023-02-24 13:47:58,747][11167] Updated weights for policy 0, policy_version 540 (0.0030) +[2023-02-24 13:48:02,940][00980] Fps is (10 sec: 4505.9, 60 sec: 3891.2, 300 sec: 3873.8). Total num frames: 2228224. Throughput: 0: 1009.2. Samples: 558208. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 13:48:02,942][00980] Avg episode reward: [(0, '21.654')] +[2023-02-24 13:48:02,953][11152] Saving new best policy, reward=21.654! +[2023-02-24 13:48:07,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3891.3, 300 sec: 3873.8). Total num frames: 2244608. Throughput: 0: 981.4. Samples: 560588. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 13:48:07,942][00980] Avg episode reward: [(0, '21.914')] +[2023-02-24 13:48:07,952][11152] Saving new best policy, reward=21.914! +[2023-02-24 13:48:09,868][11167] Updated weights for policy 0, policy_version 550 (0.0016) +[2023-02-24 13:48:12,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3959.5, 300 sec: 3846.1). Total num frames: 2260992. Throughput: 0: 955.5. Samples: 565114. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 13:48:12,942][00980] Avg episode reward: [(0, '22.633')] +[2023-02-24 13:48:12,955][11152] Saving new best policy, reward=22.633! +[2023-02-24 13:48:17,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3959.5, 300 sec: 3860.0). Total num frames: 2285568. Throughput: 0: 1003.4. Samples: 571850. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 13:48:17,942][00980] Avg episode reward: [(0, '22.016')] +[2023-02-24 13:48:19,372][11167] Updated weights for policy 0, policy_version 560 (0.0027) +[2023-02-24 13:48:22,940][00980] Fps is (10 sec: 4915.2, 60 sec: 3959.5, 300 sec: 3887.7). Total num frames: 2310144. Throughput: 0: 1006.8. Samples: 575496. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:48:22,942][00980] Avg episode reward: [(0, '21.889')] +[2023-02-24 13:48:27,945][00980] Fps is (10 sec: 3684.5, 60 sec: 3890.9, 300 sec: 3873.8). Total num frames: 2322432. Throughput: 0: 968.2. Samples: 581066. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:48:27,948][00980] Avg episode reward: [(0, '23.015')] +[2023-02-24 13:48:27,951][11152] Saving new best policy, reward=23.015! +[2023-02-24 13:48:31,140][11167] Updated weights for policy 0, policy_version 570 (0.0022) +[2023-02-24 13:48:32,940][00980] Fps is (10 sec: 2867.2, 60 sec: 3891.2, 300 sec: 3846.1). Total num frames: 2338816. Throughput: 0: 956.6. Samples: 585642. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:48:32,942][00980] Avg episode reward: [(0, '24.470')] +[2023-02-24 13:48:32,963][11152] Saving new best policy, reward=24.470! +[2023-02-24 13:48:37,940][00980] Fps is (10 sec: 4098.1, 60 sec: 3891.2, 300 sec: 3860.0). Total num frames: 2363392. Throughput: 0: 985.7. Samples: 589160. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:48:37,942][00980] Avg episode reward: [(0, '23.279')] +[2023-02-24 13:48:40,147][11167] Updated weights for policy 0, policy_version 580 (0.0017) +[2023-02-24 13:48:42,940][00980] Fps is (10 sec: 4915.2, 60 sec: 3959.5, 300 sec: 3887.7). Total num frames: 2387968. Throughput: 0: 1008.7. Samples: 596350. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:48:42,942][00980] Avg episode reward: [(0, '23.115')] +[2023-02-24 13:48:47,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3891.2, 300 sec: 3873.9). Total num frames: 2400256. Throughput: 0: 956.3. Samples: 601242. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:48:47,948][00980] Avg episode reward: [(0, '22.919')] +[2023-02-24 13:48:52,332][11167] Updated weights for policy 0, policy_version 590 (0.0013) +[2023-02-24 13:48:52,940][00980] Fps is (10 sec: 2867.2, 60 sec: 3891.2, 300 sec: 3860.0). Total num frames: 2416640. Throughput: 0: 953.6. Samples: 603498. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:48:52,947][00980] Avg episode reward: [(0, '22.899')] +[2023-02-24 13:48:57,940][00980] Fps is (10 sec: 4096.1, 60 sec: 3891.2, 300 sec: 3860.0). Total num frames: 2441216. Throughput: 0: 996.4. Samples: 609954. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:48:57,947][00980] Avg episode reward: [(0, '20.811')] +[2023-02-24 13:49:00,802][11167] Updated weights for policy 0, policy_version 600 (0.0014) +[2023-02-24 13:49:02,940][00980] Fps is (10 sec: 4915.3, 60 sec: 3959.5, 300 sec: 3887.7). Total num frames: 2465792. Throughput: 0: 1005.1. Samples: 617078. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:49:02,946][00980] Avg episode reward: [(0, '19.670')] +[2023-02-24 13:49:07,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3959.5, 300 sec: 3887.7). Total num frames: 2482176. Throughput: 0: 975.1. Samples: 619374. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:49:07,947][00980] Avg episode reward: [(0, '19.588')] +[2023-02-24 13:49:12,935][11167] Updated weights for policy 0, policy_version 610 (0.0021) +[2023-02-24 13:49:12,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3959.5, 300 sec: 3873.8). Total num frames: 2498560. Throughput: 0: 955.4. Samples: 624052. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:49:12,944][00980] Avg episode reward: [(0, '19.264')] +[2023-02-24 13:49:17,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3891.2, 300 sec: 3860.0). Total num frames: 2519040. Throughput: 0: 1006.2. Samples: 630922. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:49:17,942][00980] Avg episode reward: [(0, '18.994')] +[2023-02-24 13:49:21,487][11167] Updated weights for policy 0, policy_version 620 (0.0025) +[2023-02-24 13:49:22,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3891.2, 300 sec: 3887.7). Total num frames: 2543616. Throughput: 0: 1008.9. Samples: 634562. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:49:22,944][00980] Avg episode reward: [(0, '19.400')] +[2023-02-24 13:49:27,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3959.8, 300 sec: 3901.6). Total num frames: 2560000. Throughput: 0: 966.9. Samples: 639860. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 13:49:27,947][00980] Avg episode reward: [(0, '19.749')] +[2023-02-24 13:49:32,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3959.5, 300 sec: 3873.8). Total num frames: 2576384. Throughput: 0: 963.9. Samples: 644616. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:49:32,942][00980] Avg episode reward: [(0, '21.772')] +[2023-02-24 13:49:33,630][11167] Updated weights for policy 0, policy_version 630 (0.0034) +[2023-02-24 13:49:37,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3959.5, 300 sec: 3873.8). Total num frames: 2600960. Throughput: 0: 992.8. Samples: 648176. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:49:37,942][00980] Avg episode reward: [(0, '23.388')] +[2023-02-24 13:49:42,134][11167] Updated weights for policy 0, policy_version 640 (0.0037) +[2023-02-24 13:49:42,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3891.2, 300 sec: 3887.7). Total num frames: 2621440. Throughput: 0: 1010.4. Samples: 655422. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:49:42,945][00980] Avg episode reward: [(0, '22.442')] +[2023-02-24 13:49:42,956][11152] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000640_2621440.pth... +[2023-02-24 13:49:43,091][11152] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000411_1683456.pth +[2023-02-24 13:49:47,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3959.5, 300 sec: 3901.6). Total num frames: 2637824. Throughput: 0: 953.8. Samples: 660000. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:49:47,945][00980] Avg episode reward: [(0, '24.144')] +[2023-02-24 13:49:52,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3959.5, 300 sec: 3873.8). Total num frames: 2654208. Throughput: 0: 951.3. Samples: 662184. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:49:52,942][00980] Avg episode reward: [(0, '23.765')] +[2023-02-24 13:49:54,499][11167] Updated weights for policy 0, policy_version 650 (0.0033) +[2023-02-24 13:49:57,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3891.2, 300 sec: 3860.0). Total num frames: 2674688. Throughput: 0: 993.3. Samples: 668752. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:49:57,942][00980] Avg episode reward: [(0, '22.009')] +[2023-02-24 13:50:02,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3891.2, 300 sec: 3887.7). Total num frames: 2699264. Throughput: 0: 994.2. Samples: 675660. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:50:02,944][00980] Avg episode reward: [(0, '20.942')] +[2023-02-24 13:50:03,645][11167] Updated weights for policy 0, policy_version 660 (0.0015) +[2023-02-24 13:50:07,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.2, 300 sec: 3887.7). Total num frames: 2715648. Throughput: 0: 963.9. Samples: 677938. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:50:07,946][00980] Avg episode reward: [(0, '21.439')] +[2023-02-24 13:50:12,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3891.2, 300 sec: 3873.8). Total num frames: 2732032. Throughput: 0: 949.9. Samples: 682606. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:50:12,947][00980] Avg episode reward: [(0, '21.345')] +[2023-02-24 13:50:15,229][11167] Updated weights for policy 0, policy_version 670 (0.0014) +[2023-02-24 13:50:17,940][00980] Fps is (10 sec: 4095.9, 60 sec: 3959.4, 300 sec: 3873.8). Total num frames: 2756608. Throughput: 0: 1000.8. Samples: 689654. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:50:17,942][00980] Avg episode reward: [(0, '20.952')] +[2023-02-24 13:50:22,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3891.2, 300 sec: 3887.7). Total num frames: 2777088. Throughput: 0: 1000.1. Samples: 693182. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:50:22,943][00980] Avg episode reward: [(0, '20.930')] +[2023-02-24 13:50:24,913][11167] Updated weights for policy 0, policy_version 680 (0.0015) +[2023-02-24 13:50:27,940][00980] Fps is (10 sec: 3686.5, 60 sec: 3891.2, 300 sec: 3887.7). Total num frames: 2793472. Throughput: 0: 949.3. Samples: 698142. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:50:27,946][00980] Avg episode reward: [(0, '22.144')] +[2023-02-24 13:50:32,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3891.2, 300 sec: 3887.7). Total num frames: 2809856. Throughput: 0: 957.6. Samples: 703092. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:50:32,946][00980] Avg episode reward: [(0, '22.441')] +[2023-02-24 13:50:36,209][11167] Updated weights for policy 0, policy_version 690 (0.0049) +[2023-02-24 13:50:37,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.2, 300 sec: 3901.6). Total num frames: 2834432. Throughput: 0: 988.8. Samples: 706678. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 13:50:37,942][00980] Avg episode reward: [(0, '23.206')] +[2023-02-24 13:50:42,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3891.2, 300 sec: 3915.5). Total num frames: 2854912. Throughput: 0: 1003.4. Samples: 713906. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:50:42,950][00980] Avg episode reward: [(0, '23.539')] +[2023-02-24 13:50:46,085][11167] Updated weights for policy 0, policy_version 700 (0.0017) +[2023-02-24 13:50:47,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3891.2, 300 sec: 3929.4). Total num frames: 2871296. Throughput: 0: 955.2. Samples: 718644. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:50:47,942][00980] Avg episode reward: [(0, '24.095')] +[2023-02-24 13:50:52,940][00980] Fps is (10 sec: 3276.7, 60 sec: 3891.2, 300 sec: 3901.6). Total num frames: 2887680. Throughput: 0: 955.8. Samples: 720950. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:50:52,947][00980] Avg episode reward: [(0, '26.693')] +[2023-02-24 13:50:52,965][11152] Saving new best policy, reward=26.693! +[2023-02-24 13:50:56,683][11167] Updated weights for policy 0, policy_version 710 (0.0016) +[2023-02-24 13:50:57,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3959.5, 300 sec: 3901.6). Total num frames: 2912256. Throughput: 0: 998.4. Samples: 727534. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:50:57,946][00980] Avg episode reward: [(0, '26.035')] +[2023-02-24 13:51:02,940][00980] Fps is (10 sec: 4505.7, 60 sec: 3891.2, 300 sec: 3915.5). Total num frames: 2932736. Throughput: 0: 996.5. Samples: 734498. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:51:02,943][00980] Avg episode reward: [(0, '25.625')] +[2023-02-24 13:51:07,116][11167] Updated weights for policy 0, policy_version 720 (0.0012) +[2023-02-24 13:51:07,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3891.2, 300 sec: 3929.4). Total num frames: 2949120. Throughput: 0: 968.7. Samples: 736774. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:51:07,945][00980] Avg episode reward: [(0, '24.872')] +[2023-02-24 13:51:12,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3891.2, 300 sec: 3901.6). Total num frames: 2965504. Throughput: 0: 961.1. Samples: 741390. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:51:12,947][00980] Avg episode reward: [(0, '25.277')] +[2023-02-24 13:51:17,311][11167] Updated weights for policy 0, policy_version 730 (0.0035) +[2023-02-24 13:51:17,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.2, 300 sec: 3901.6). Total num frames: 2990080. Throughput: 0: 1012.2. Samples: 748642. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:51:17,943][00980] Avg episode reward: [(0, '24.269')] +[2023-02-24 13:51:22,940][00980] Fps is (10 sec: 4915.1, 60 sec: 3959.4, 300 sec: 3929.4). Total num frames: 3014656. Throughput: 0: 1012.3. Samples: 752230. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:51:22,944][00980] Avg episode reward: [(0, '24.348')] +[2023-02-24 13:51:27,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3891.2, 300 sec: 3929.4). Total num frames: 3026944. Throughput: 0: 961.3. Samples: 757164. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:51:27,948][00980] Avg episode reward: [(0, '25.070')] +[2023-02-24 13:51:28,432][11167] Updated weights for policy 0, policy_version 740 (0.0012) +[2023-02-24 13:51:32,940][00980] Fps is (10 sec: 2867.2, 60 sec: 3891.2, 300 sec: 3901.6). Total num frames: 3043328. Throughput: 0: 964.5. Samples: 762048. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:51:32,948][00980] Avg episode reward: [(0, '25.088')] +[2023-02-24 13:51:37,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.2, 300 sec: 3901.6). Total num frames: 3067904. Throughput: 0: 992.7. Samples: 765622. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:51:37,942][00980] Avg episode reward: [(0, '25.119')] +[2023-02-24 13:51:38,118][11167] Updated weights for policy 0, policy_version 750 (0.0014) +[2023-02-24 13:51:42,944][00980] Fps is (10 sec: 4913.2, 60 sec: 3959.2, 300 sec: 3929.3). Total num frames: 3092480. Throughput: 0: 1009.5. Samples: 772966. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:51:42,947][00980] Avg episode reward: [(0, '24.609')] +[2023-02-24 13:51:42,966][11152] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000755_3092480.pth... +[2023-02-24 13:51:43,132][11152] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000525_2150400.pth +[2023-02-24 13:51:47,940][00980] Fps is (10 sec: 4095.9, 60 sec: 3959.5, 300 sec: 3943.3). Total num frames: 3108864. Throughput: 0: 958.9. Samples: 777648. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:51:47,942][00980] Avg episode reward: [(0, '24.922')] +[2023-02-24 13:51:48,996][11167] Updated weights for policy 0, policy_version 760 (0.0011) +[2023-02-24 13:51:52,940][00980] Fps is (10 sec: 3278.2, 60 sec: 3959.5, 300 sec: 3915.5). Total num frames: 3125248. Throughput: 0: 959.2. Samples: 779936. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) +[2023-02-24 13:51:52,949][00980] Avg episode reward: [(0, '24.443')] +[2023-02-24 13:51:57,940][00980] Fps is (10 sec: 4096.1, 60 sec: 3959.5, 300 sec: 3915.5). Total num frames: 3149824. Throughput: 0: 1006.7. Samples: 786692. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:51:57,943][00980] Avg episode reward: [(0, '23.121')] +[2023-02-24 13:51:58,600][11167] Updated weights for policy 0, policy_version 770 (0.0016) +[2023-02-24 13:52:02,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3959.5, 300 sec: 3929.4). Total num frames: 3170304. Throughput: 0: 1000.4. Samples: 793662. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 13:52:02,942][00980] Avg episode reward: [(0, '23.086')] +[2023-02-24 13:52:07,940][00980] Fps is (10 sec: 3686.3, 60 sec: 3959.4, 300 sec: 3943.3). Total num frames: 3186688. Throughput: 0: 971.6. Samples: 795950. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:52:07,950][00980] Avg episode reward: [(0, '22.290')] +[2023-02-24 13:52:10,165][11167] Updated weights for policy 0, policy_version 780 (0.0025) +[2023-02-24 13:52:12,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3959.5, 300 sec: 3915.5). Total num frames: 3203072. Throughput: 0: 963.8. Samples: 800536. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:52:12,942][00980] Avg episode reward: [(0, '21.696')] +[2023-02-24 13:52:17,940][00980] Fps is (10 sec: 4096.1, 60 sec: 3959.5, 300 sec: 3915.5). Total num frames: 3227648. Throughput: 0: 1018.3. Samples: 807870. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:52:17,946][00980] Avg episode reward: [(0, '21.529')] +[2023-02-24 13:52:19,091][11167] Updated weights for policy 0, policy_version 790 (0.0020) +[2023-02-24 13:52:22,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3891.2, 300 sec: 3929.4). Total num frames: 3248128. Throughput: 0: 1019.9. Samples: 811518. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:52:22,943][00980] Avg episode reward: [(0, '22.744')] +[2023-02-24 13:52:27,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3959.5, 300 sec: 3929.4). Total num frames: 3264512. Throughput: 0: 967.6. Samples: 816506. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:52:27,942][00980] Avg episode reward: [(0, '23.093')] +[2023-02-24 13:52:30,941][11167] Updated weights for policy 0, policy_version 800 (0.0033) +[2023-02-24 13:52:32,940][00980] Fps is (10 sec: 3686.4, 60 sec: 4027.8, 300 sec: 3915.5). Total num frames: 3284992. Throughput: 0: 976.4. Samples: 821586. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:52:32,947][00980] Avg episode reward: [(0, '24.660')] +[2023-02-24 13:52:37,940][00980] Fps is (10 sec: 4505.5, 60 sec: 4027.7, 300 sec: 3929.4). Total num frames: 3309568. Throughput: 0: 1006.2. Samples: 825214. Policy #0 lag: (min: 0.0, avg: 0.3, max: 1.0) +[2023-02-24 13:52:37,947][00980] Avg episode reward: [(0, '25.534')] +[2023-02-24 13:52:39,715][11167] Updated weights for policy 0, policy_version 810 (0.0028) +[2023-02-24 13:52:42,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3959.7, 300 sec: 3943.3). Total num frames: 3330048. Throughput: 0: 1014.2. Samples: 832332. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:52:42,943][00980] Avg episode reward: [(0, '25.907')] +[2023-02-24 13:52:47,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3891.2, 300 sec: 3929.4). Total num frames: 3342336. Throughput: 0: 960.6. Samples: 836890. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:52:47,944][00980] Avg episode reward: [(0, '26.039')] +[2023-02-24 13:52:51,855][11167] Updated weights for policy 0, policy_version 820 (0.0023) +[2023-02-24 13:52:52,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3959.5, 300 sec: 3915.5). Total num frames: 3362816. Throughput: 0: 959.3. Samples: 839118. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:52:52,942][00980] Avg episode reward: [(0, '26.542')] +[2023-02-24 13:52:57,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3959.5, 300 sec: 3929.4). Total num frames: 3387392. Throughput: 0: 1013.3. Samples: 846134. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:52:57,947][00980] Avg episode reward: [(0, '25.855')] +[2023-02-24 13:53:00,300][11167] Updated weights for policy 0, policy_version 830 (0.0014) +[2023-02-24 13:53:02,942][00980] Fps is (10 sec: 4504.6, 60 sec: 3959.3, 300 sec: 3943.2). Total num frames: 3407872. Throughput: 0: 997.3. Samples: 852752. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:53:02,950][00980] Avg episode reward: [(0, '25.344')] +[2023-02-24 13:53:07,945][00980] Fps is (10 sec: 3684.5, 60 sec: 3959.1, 300 sec: 3943.2). Total num frames: 3424256. Throughput: 0: 967.7. Samples: 855068. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:53:07,950][00980] Avg episode reward: [(0, '25.157')] +[2023-02-24 13:53:12,322][11167] Updated weights for policy 0, policy_version 840 (0.0017) +[2023-02-24 13:53:12,940][00980] Fps is (10 sec: 3277.5, 60 sec: 3959.5, 300 sec: 3915.5). Total num frames: 3440640. Throughput: 0: 966.1. Samples: 859980. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 13:53:12,942][00980] Avg episode reward: [(0, '24.986')] +[2023-02-24 13:53:17,940][00980] Fps is (10 sec: 4098.0, 60 sec: 3959.5, 300 sec: 3915.5). Total num frames: 3465216. Throughput: 0: 1015.6. Samples: 867288. Policy #0 lag: (min: 0.0, avg: 0.6, max: 1.0) +[2023-02-24 13:53:17,942][00980] Avg episode reward: [(0, '23.994')] +[2023-02-24 13:53:20,771][11167] Updated weights for policy 0, policy_version 850 (0.0017) +[2023-02-24 13:53:22,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3959.5, 300 sec: 3943.3). Total num frames: 3485696. Throughput: 0: 1014.5. Samples: 870868. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:53:22,946][00980] Avg episode reward: [(0, '23.515')] +[2023-02-24 13:53:27,940][00980] Fps is (10 sec: 3686.5, 60 sec: 3959.5, 300 sec: 3943.3). Total num frames: 3502080. Throughput: 0: 961.0. Samples: 875576. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:53:27,942][00980] Avg episode reward: [(0, '22.749')] +[2023-02-24 13:53:32,844][11167] Updated weights for policy 0, policy_version 860 (0.0013) +[2023-02-24 13:53:32,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3959.5, 300 sec: 3929.4). Total num frames: 3522560. Throughput: 0: 980.3. Samples: 881004. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:53:32,943][00980] Avg episode reward: [(0, '24.068')] +[2023-02-24 13:53:37,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.2, 300 sec: 3915.5). Total num frames: 3543040. Throughput: 0: 1011.7. Samples: 884644. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:53:37,942][00980] Avg episode reward: [(0, '24.460')] +[2023-02-24 13:53:41,525][11167] Updated weights for policy 0, policy_version 870 (0.0016) +[2023-02-24 13:53:42,944][00980] Fps is (10 sec: 4503.7, 60 sec: 3959.2, 300 sec: 3957.1). Total num frames: 3567616. Throughput: 0: 1006.7. Samples: 891438. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:53:42,946][00980] Avg episode reward: [(0, '24.611')] +[2023-02-24 13:53:42,962][11152] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000871_3567616.pth... +[2023-02-24 13:53:43,098][11152] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000640_2621440.pth +[2023-02-24 13:53:47,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3959.5, 300 sec: 3943.3). Total num frames: 3579904. Throughput: 0: 961.9. Samples: 896036. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:53:47,956][00980] Avg episode reward: [(0, '24.148')] +[2023-02-24 13:53:52,940][00980] Fps is (10 sec: 3278.2, 60 sec: 3959.5, 300 sec: 3929.4). Total num frames: 3600384. Throughput: 0: 962.2. Samples: 898364. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:53:52,947][00980] Avg episode reward: [(0, '24.942')] +[2023-02-24 13:53:53,338][11167] Updated weights for policy 0, policy_version 880 (0.0013) +[2023-02-24 13:53:57,940][00980] Fps is (10 sec: 4505.7, 60 sec: 3959.5, 300 sec: 3929.4). Total num frames: 3624960. Throughput: 0: 1015.3. Samples: 905668. Policy #0 lag: (min: 0.0, avg: 0.4, max: 2.0) +[2023-02-24 13:53:57,943][00980] Avg episode reward: [(0, '26.245')] +[2023-02-24 13:54:02,912][11167] Updated weights for policy 0, policy_version 890 (0.0014) +[2023-02-24 13:54:02,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3959.6, 300 sec: 3943.3). Total num frames: 3645440. Throughput: 0: 989.6. Samples: 911818. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:54:02,942][00980] Avg episode reward: [(0, '24.977')] +[2023-02-24 13:54:07,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3891.5, 300 sec: 3929.4). Total num frames: 3657728. Throughput: 0: 959.3. Samples: 914038. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:54:07,948][00980] Avg episode reward: [(0, '25.792')] +[2023-02-24 13:54:12,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3959.5, 300 sec: 3929.4). Total num frames: 3678208. Throughput: 0: 971.8. Samples: 919308. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 13:54:12,949][00980] Avg episode reward: [(0, '25.107')] +[2023-02-24 13:54:14,064][11167] Updated weights for policy 0, policy_version 900 (0.0025) +[2023-02-24 13:54:17,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3959.5, 300 sec: 3929.4). Total num frames: 3702784. Throughput: 0: 1013.9. Samples: 926628. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:54:17,943][00980] Avg episode reward: [(0, '26.024')] +[2023-02-24 13:54:22,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3959.5, 300 sec: 3943.3). Total num frames: 3723264. Throughput: 0: 1010.1. Samples: 930100. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:54:22,942][00980] Avg episode reward: [(0, '25.060')] +[2023-02-24 13:54:23,755][11167] Updated weights for policy 0, policy_version 910 (0.0012) +[2023-02-24 13:54:27,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3959.5, 300 sec: 3943.3). Total num frames: 3739648. Throughput: 0: 961.2. Samples: 934686. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:54:27,946][00980] Avg episode reward: [(0, '25.466')] +[2023-02-24 13:54:32,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3959.5, 300 sec: 3929.4). Total num frames: 3760128. Throughput: 0: 986.6. Samples: 940434. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:54:32,945][00980] Avg episode reward: [(0, '26.161')] +[2023-02-24 13:54:34,537][11167] Updated weights for policy 0, policy_version 920 (0.0017) +[2023-02-24 13:54:37,940][00980] Fps is (10 sec: 4505.6, 60 sec: 4027.7, 300 sec: 3943.3). Total num frames: 3784704. Throughput: 0: 1016.0. Samples: 944084. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:54:37,944][00980] Avg episode reward: [(0, '26.766')] +[2023-02-24 13:54:37,949][11152] Saving new best policy, reward=26.766! +[2023-02-24 13:54:42,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.5, 300 sec: 3943.3). Total num frames: 3801088. Throughput: 0: 998.6. Samples: 950606. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:54:42,947][00980] Avg episode reward: [(0, '26.990')] +[2023-02-24 13:54:42,958][11152] Saving new best policy, reward=26.990! +[2023-02-24 13:54:44,675][11167] Updated weights for policy 0, policy_version 930 (0.0017) +[2023-02-24 13:54:47,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3959.5, 300 sec: 3943.3). Total num frames: 3817472. Throughput: 0: 961.6. Samples: 955088. Policy #0 lag: (min: 0.0, avg: 0.4, max: 1.0) +[2023-02-24 13:54:47,944][00980] Avg episode reward: [(0, '27.957')] +[2023-02-24 13:54:47,950][11152] Saving new best policy, reward=27.957! +[2023-02-24 13:54:52,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3959.5, 300 sec: 3943.3). Total num frames: 3837952. Throughput: 0: 970.5. Samples: 957710. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:54:52,942][00980] Avg episode reward: [(0, '26.032')] +[2023-02-24 13:54:55,178][11167] Updated weights for policy 0, policy_version 940 (0.0032) +[2023-02-24 13:54:57,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3959.5, 300 sec: 3943.3). Total num frames: 3862528. Throughput: 0: 1016.5. Samples: 965052. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:54:57,942][00980] Avg episode reward: [(0, '25.783')] +[2023-02-24 13:55:02,940][00980] Fps is (10 sec: 4096.0, 60 sec: 3891.2, 300 sec: 3943.3). Total num frames: 3878912. Throughput: 0: 988.7. Samples: 971118. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:55:02,944][00980] Avg episode reward: [(0, '25.187')] +[2023-02-24 13:55:05,669][11167] Updated weights for policy 0, policy_version 950 (0.0013) +[2023-02-24 13:55:07,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3959.5, 300 sec: 3943.3). Total num frames: 3895296. Throughput: 0: 961.2. Samples: 973356. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:55:07,949][00980] Avg episode reward: [(0, '24.429')] +[2023-02-24 13:55:12,940][00980] Fps is (10 sec: 3686.4, 60 sec: 3959.5, 300 sec: 3929.4). Total num frames: 3915776. Throughput: 0: 982.3. Samples: 978890. Policy #0 lag: (min: 0.0, avg: 0.6, max: 2.0) +[2023-02-24 13:55:12,942][00980] Avg episode reward: [(0, '23.922')] +[2023-02-24 13:55:15,707][11167] Updated weights for policy 0, policy_version 960 (0.0025) +[2023-02-24 13:55:17,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3959.5, 300 sec: 3943.3). Total num frames: 3940352. Throughput: 0: 1015.5. Samples: 986132. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:55:17,943][00980] Avg episode reward: [(0, '25.560')] +[2023-02-24 13:55:22,940][00980] Fps is (10 sec: 4505.6, 60 sec: 3959.5, 300 sec: 3957.2). Total num frames: 3960832. Throughput: 0: 1002.9. Samples: 989216. Policy #0 lag: (min: 0.0, avg: 0.5, max: 2.0) +[2023-02-24 13:55:22,943][00980] Avg episode reward: [(0, '25.104')] +[2023-02-24 13:55:26,867][11167] Updated weights for policy 0, policy_version 970 (0.0013) +[2023-02-24 13:55:27,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3891.2, 300 sec: 3943.3). Total num frames: 3973120. Throughput: 0: 961.3. Samples: 993864. Policy #0 lag: (min: 0.0, avg: 0.5, max: 1.0) +[2023-02-24 13:55:27,948][00980] Avg episode reward: [(0, '26.268')] +[2023-02-24 13:55:32,940][00980] Fps is (10 sec: 3276.8, 60 sec: 3891.2, 300 sec: 3929.4). Total num frames: 3993600. Throughput: 0: 993.0. Samples: 999774. Policy #0 lag: (min: 0.0, avg: 0.7, max: 2.0) +[2023-02-24 13:55:32,947][00980] Avg episode reward: [(0, '25.975')] +[2023-02-24 13:55:34,585][11152] Stopping Batcher_0... +[2023-02-24 13:55:34,586][00980] Component Batcher_0 stopped! +[2023-02-24 13:55:34,589][11152] Loop batcher_evt_loop terminating... +[2023-02-24 13:55:34,588][11152] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... +[2023-02-24 13:55:34,643][11167] Weights refcount: 2 0 +[2023-02-24 13:55:34,658][00980] Component InferenceWorker_p0-w0 stopped! +[2023-02-24 13:55:34,660][11167] Stopping InferenceWorker_p0-w0... +[2023-02-24 13:55:34,661][11167] Loop inference_proc0-0_evt_loop terminating... +[2023-02-24 13:55:34,674][11171] Stopping RolloutWorker_w5... +[2023-02-24 13:55:34,674][00980] Component RolloutWorker_w5 stopped! +[2023-02-24 13:55:34,684][11173] Stopping RolloutWorker_w3... +[2023-02-24 13:55:34,684][00980] Component RolloutWorker_w4 stopped! +[2023-02-24 13:55:34,686][00980] Component RolloutWorker_w3 stopped! +[2023-02-24 13:55:34,683][11170] Stopping RolloutWorker_w4... +[2023-02-24 13:55:34,692][00980] Component RolloutWorker_w0 stopped! +[2023-02-24 13:55:34,694][11169] Stopping RolloutWorker_w1... +[2023-02-24 13:55:34,695][00980] Component RolloutWorker_w1 stopped! +[2023-02-24 13:55:34,689][11170] Loop rollout_proc4_evt_loop terminating... +[2023-02-24 13:55:34,694][11166] Stopping RolloutWorker_w0... +[2023-02-24 13:55:34,701][00980] Component RolloutWorker_w2 stopped! +[2023-02-24 13:55:34,701][11168] Stopping RolloutWorker_w2... +[2023-02-24 13:55:34,710][11174] Stopping RolloutWorker_w7... +[2023-02-24 13:55:34,710][11174] Loop rollout_proc7_evt_loop terminating... +[2023-02-24 13:55:34,709][00980] Component RolloutWorker_w6 stopped! +[2023-02-24 13:55:34,680][11171] Loop rollout_proc5_evt_loop terminating... +[2023-02-24 13:55:34,709][11173] Loop rollout_proc3_evt_loop terminating... +[2023-02-24 13:55:34,709][11172] Stopping RolloutWorker_w6... +[2023-02-24 13:55:34,711][00980] Component RolloutWorker_w7 stopped! +[2023-02-24 13:55:34,695][11169] Loop rollout_proc1_evt_loop terminating... +[2023-02-24 13:55:34,700][11166] Loop rollout_proc0_evt_loop terminating... +[2023-02-24 13:55:34,717][11168] Loop rollout_proc2_evt_loop terminating... +[2023-02-24 13:55:34,719][11172] Loop rollout_proc6_evt_loop terminating... +[2023-02-24 13:55:34,781][11152] Removing /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000755_3092480.pth +[2023-02-24 13:55:34,793][11152] Saving /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... +[2023-02-24 13:55:34,954][00980] Component LearnerWorker_p0 stopped! +[2023-02-24 13:55:34,963][00980] Waiting for process learner_proc0 to stop... +[2023-02-24 13:55:34,967][11152] Stopping LearnerWorker_p0... +[2023-02-24 13:55:34,968][11152] Loop learner_proc0_evt_loop terminating... +[2023-02-24 13:55:36,742][00980] Waiting for process inference_proc0-0 to join... +[2023-02-24 13:55:37,070][00980] Waiting for process rollout_proc0 to join... +[2023-02-24 13:55:37,548][00980] Waiting for process rollout_proc1 to join... +[2023-02-24 13:55:37,549][00980] Waiting for process rollout_proc2 to join... +[2023-02-24 13:55:37,551][00980] Waiting for process rollout_proc3 to join... +[2023-02-24 13:55:37,552][00980] Waiting for process rollout_proc4 to join... +[2023-02-24 13:55:37,570][00980] Waiting for process rollout_proc5 to join... +[2023-02-24 13:55:37,571][00980] Waiting for process rollout_proc6 to join... +[2023-02-24 13:55:37,572][00980] Waiting for process rollout_proc7 to join... +[2023-02-24 13:55:37,573][00980] Batcher 0 profile tree view: +batching: 25.3280, releasing_batches: 0.0221 +[2023-02-24 13:55:37,575][00980] InferenceWorker_p0-w0 profile tree view: +wait_policy: 0.0005 + wait_policy_total: 498.3727 +update_model: 7.4088 + weight_update: 0.0013 +one_step: 0.0023 + handle_policy_step: 495.8979 + deserialize: 14.1770, stack: 2.8848, obs_to_device_normalize: 112.3387, forward: 236.1196, send_messages: 25.3594 + prepare_outputs: 80.6792 + to_cpu: 50.7526 +[2023-02-24 13:55:37,576][00980] Learner 0 profile tree view: +misc: 0.0053, prepare_batch: 17.0874 +train: 74.1658 + epoch_init: 0.0057, minibatch_init: 0.0202, losses_postprocess: 0.5531, kl_divergence: 0.5942, after_optimizer: 32.2145 + calculate_losses: 25.9284 + losses_init: 0.0048, forward_head: 1.6499, bptt_initial: 17.2608, tail: 0.9288, advantages_returns: 0.3566, losses: 3.2248 + bptt: 2.1849 + bptt_forward_core: 2.1100 + update: 14.2904 + clip: 1.4789 +[2023-02-24 13:55:37,578][00980] RolloutWorker_w0 profile tree view: +wait_for_trajectories: 0.3031, enqueue_policy_requests: 129.9487, env_step: 791.6916, overhead: 18.9921, complete_rollouts: 6.7884 +save_policy_outputs: 19.2065 + split_output_tensors: 9.2842 +[2023-02-24 13:55:37,579][00980] RolloutWorker_w7 profile tree view: +wait_for_trajectories: 0.3690, enqueue_policy_requests: 129.6848, env_step: 791.0784, overhead: 18.7556, complete_rollouts: 6.6190 +save_policy_outputs: 18.7805 + split_output_tensors: 9.0138 +[2023-02-24 13:55:37,581][00980] Loop Runner_EvtLoop terminating... +[2023-02-24 13:55:37,583][00980] Runner profile tree view: +main_loop: 1068.9353 +[2023-02-24 13:55:37,585][00980] Collected {0: 4005888}, FPS: 3747.5 +[2023-02-24 13:55:37,828][00980] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json +[2023-02-24 13:55:37,830][00980] Overriding arg 'num_workers' with value 1 passed from command line +[2023-02-24 13:55:37,833][00980] Adding new argument 'no_render'=True that is not in the saved config file! +[2023-02-24 13:55:37,836][00980] Adding new argument 'save_video'=True that is not in the saved config file! +[2023-02-24 13:55:37,838][00980] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! +[2023-02-24 13:55:37,840][00980] Adding new argument 'video_name'=None that is not in the saved config file! +[2023-02-24 13:55:37,842][00980] Adding new argument 'max_num_frames'=1000000000.0 that is not in the saved config file! +[2023-02-24 13:55:37,843][00980] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! +[2023-02-24 13:55:37,844][00980] Adding new argument 'push_to_hub'=False that is not in the saved config file! +[2023-02-24 13:55:37,845][00980] Adding new argument 'hf_repository'=None that is not in the saved config file! +[2023-02-24 13:55:37,847][00980] Adding new argument 'policy_index'=0 that is not in the saved config file! +[2023-02-24 13:55:37,849][00980] Adding new argument 'eval_deterministic'=False that is not in the saved config file! +[2023-02-24 13:55:37,850][00980] Adding new argument 'train_script'=None that is not in the saved config file! +[2023-02-24 13:55:37,851][00980] Adding new argument 'enjoy_script'=None that is not in the saved config file! +[2023-02-24 13:55:37,853][00980] Using frameskip 1 and render_action_repeat=4 for evaluation +[2023-02-24 13:55:37,880][00980] Doom resolution: 160x120, resize resolution: (128, 72) +[2023-02-24 13:55:37,882][00980] RunningMeanStd input shape: (3, 72, 128) +[2023-02-24 13:55:37,884][00980] RunningMeanStd input shape: (1,) +[2023-02-24 13:55:37,900][00980] ConvEncoder: input_channels=3 +[2023-02-24 13:55:38,593][00980] Conv encoder output size: 512 +[2023-02-24 13:55:38,595][00980] Policy head output size: 512 +[2023-02-24 13:55:41,532][00980] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... +[2023-02-24 13:55:43,247][00980] Num frames 100... +[2023-02-24 13:55:43,370][00980] Num frames 200... +[2023-02-24 13:55:43,480][00980] Num frames 300... +[2023-02-24 13:55:43,592][00980] Num frames 400... +[2023-02-24 13:55:43,709][00980] Num frames 500... +[2023-02-24 13:55:43,824][00980] Num frames 600... +[2023-02-24 13:55:43,947][00980] Num frames 700... +[2023-02-24 13:55:44,057][00980] Num frames 800... +[2023-02-24 13:55:44,170][00980] Num frames 900... +[2023-02-24 13:55:44,283][00980] Num frames 1000... +[2023-02-24 13:55:44,409][00980] Num frames 1100... +[2023-02-24 13:55:44,526][00980] Num frames 1200... +[2023-02-24 13:55:44,643][00980] Num frames 1300... +[2023-02-24 13:55:44,757][00980] Num frames 1400... +[2023-02-24 13:55:44,870][00980] Num frames 1500... +[2023-02-24 13:55:44,982][00980] Num frames 1600... +[2023-02-24 13:55:45,098][00980] Num frames 1700... +[2023-02-24 13:55:45,212][00980] Num frames 1800... +[2023-02-24 13:55:45,331][00980] Num frames 1900... +[2023-02-24 13:55:45,452][00980] Num frames 2000... +[2023-02-24 13:55:45,569][00980] Num frames 2100... +[2023-02-24 13:55:45,622][00980] Avg episode rewards: #0: 55.999, true rewards: #0: 21.000 +[2023-02-24 13:55:45,624][00980] Avg episode reward: 55.999, avg true_objective: 21.000 +[2023-02-24 13:55:45,739][00980] Num frames 2200... +[2023-02-24 13:55:45,851][00980] Num frames 2300... +[2023-02-24 13:55:45,962][00980] Num frames 2400... +[2023-02-24 13:55:46,071][00980] Num frames 2500... +[2023-02-24 13:55:46,192][00980] Num frames 2600... +[2023-02-24 13:55:46,311][00980] Num frames 2700... +[2023-02-24 13:55:46,430][00980] Num frames 2800... +[2023-02-24 13:55:46,541][00980] Num frames 2900... +[2023-02-24 13:55:46,668][00980] Num frames 3000... +[2023-02-24 13:55:46,778][00980] Num frames 3100... +[2023-02-24 13:55:46,891][00980] Num frames 3200... +[2023-02-24 13:55:47,003][00980] Num frames 3300... +[2023-02-24 13:55:47,121][00980] Num frames 3400... +[2023-02-24 13:55:47,240][00980] Num frames 3500... +[2023-02-24 13:55:47,360][00980] Num frames 3600... +[2023-02-24 13:55:47,480][00980] Num frames 3700... +[2023-02-24 13:55:47,591][00980] Num frames 3800... +[2023-02-24 13:55:47,704][00980] Num frames 3900... +[2023-02-24 13:55:47,868][00980] Avg episode rewards: #0: 52.459, true rewards: #0: 19.960 +[2023-02-24 13:55:47,869][00980] Avg episode reward: 52.459, avg true_objective: 19.960 +[2023-02-24 13:55:47,884][00980] Num frames 4000... +[2023-02-24 13:55:47,999][00980] Num frames 4100... +[2023-02-24 13:55:48,109][00980] Num frames 4200... +[2023-02-24 13:55:48,219][00980] Num frames 4300... +[2023-02-24 13:55:48,332][00980] Num frames 4400... +[2023-02-24 13:55:48,468][00980] Num frames 4500... +[2023-02-24 13:55:48,588][00980] Num frames 4600... +[2023-02-24 13:55:48,709][00980] Num frames 4700... +[2023-02-24 13:55:48,822][00980] Num frames 4800... +[2023-02-24 13:55:48,937][00980] Num frames 4900... +[2023-02-24 13:55:49,050][00980] Num frames 5000... +[2023-02-24 13:55:49,206][00980] Avg episode rewards: #0: 43.623, true rewards: #0: 16.957 +[2023-02-24 13:55:49,208][00980] Avg episode reward: 43.623, avg true_objective: 16.957 +[2023-02-24 13:55:49,228][00980] Num frames 5100... +[2023-02-24 13:55:49,341][00980] Num frames 5200... +[2023-02-24 13:55:49,466][00980] Num frames 5300... +[2023-02-24 13:55:49,578][00980] Num frames 5400... +[2023-02-24 13:55:49,690][00980] Num frames 5500... +[2023-02-24 13:55:49,804][00980] Num frames 5600... +[2023-02-24 13:55:49,968][00980] Avg episode rewards: #0: 35.492, true rewards: #0: 14.243 +[2023-02-24 13:55:49,970][00980] Avg episode reward: 35.492, avg true_objective: 14.243 +[2023-02-24 13:55:49,976][00980] Num frames 5700... +[2023-02-24 13:55:50,090][00980] Num frames 5800... +[2023-02-24 13:55:50,202][00980] Num frames 5900... +[2023-02-24 13:55:50,314][00980] Num frames 6000... +[2023-02-24 13:55:50,439][00980] Num frames 6100... +[2023-02-24 13:55:50,519][00980] Avg episode rewards: #0: 30.026, true rewards: #0: 12.226 +[2023-02-24 13:55:50,521][00980] Avg episode reward: 30.026, avg true_objective: 12.226 +[2023-02-24 13:55:50,632][00980] Num frames 6200... +[2023-02-24 13:55:50,747][00980] Num frames 6300... +[2023-02-24 13:55:50,861][00980] Num frames 6400... +[2023-02-24 13:55:50,987][00980] Num frames 6500... +[2023-02-24 13:55:51,101][00980] Num frames 6600... +[2023-02-24 13:55:51,224][00980] Num frames 6700... +[2023-02-24 13:55:51,338][00980] Num frames 6800... +[2023-02-24 13:55:51,469][00980] Num frames 6900... +[2023-02-24 13:55:51,585][00980] Num frames 7000... +[2023-02-24 13:55:51,665][00980] Avg episode rewards: #0: 28.361, true rewards: #0: 11.695 +[2023-02-24 13:55:51,669][00980] Avg episode reward: 28.361, avg true_objective: 11.695 +[2023-02-24 13:55:51,760][00980] Num frames 7100... +[2023-02-24 13:55:51,873][00980] Num frames 7200... +[2023-02-24 13:55:51,994][00980] Num frames 7300... +[2023-02-24 13:55:52,108][00980] Num frames 7400... +[2023-02-24 13:55:52,221][00980] Num frames 7500... +[2023-02-24 13:55:52,336][00980] Num frames 7600... +[2023-02-24 13:55:52,448][00980] Num frames 7700... +[2023-02-24 13:55:52,572][00980] Num frames 7800... +[2023-02-24 13:55:52,725][00980] Avg episode rewards: #0: 27.265, true rewards: #0: 11.266 +[2023-02-24 13:55:52,727][00980] Avg episode reward: 27.265, avg true_objective: 11.266 +[2023-02-24 13:55:52,746][00980] Num frames 7900... +[2023-02-24 13:55:52,858][00980] Num frames 8000... +[2023-02-24 13:55:52,967][00980] Num frames 8100... +[2023-02-24 13:55:53,081][00980] Num frames 8200... +[2023-02-24 13:55:53,228][00980] Num frames 8300... +[2023-02-24 13:55:53,398][00980] Num frames 8400... +[2023-02-24 13:55:53,602][00980] Avg episode rewards: #0: 25.492, true rewards: #0: 10.617 +[2023-02-24 13:55:53,605][00980] Avg episode reward: 25.492, avg true_objective: 10.617 +[2023-02-24 13:55:53,619][00980] Num frames 8500... +[2023-02-24 13:55:53,775][00980] Num frames 8600... +[2023-02-24 13:55:53,934][00980] Num frames 8700... +[2023-02-24 13:55:54,021][00980] Avg episode rewards: #0: 23.020, true rewards: #0: 9.687 +[2023-02-24 13:55:54,024][00980] Avg episode reward: 23.020, avg true_objective: 9.687 +[2023-02-24 13:55:54,159][00980] Num frames 8800... +[2023-02-24 13:55:54,318][00980] Num frames 8900... +[2023-02-24 13:55:54,487][00980] Num frames 9000... +[2023-02-24 13:55:54,648][00980] Num frames 9100... +[2023-02-24 13:55:54,815][00980] Num frames 9200... +[2023-02-24 13:55:54,976][00980] Num frames 9300... +[2023-02-24 13:55:55,142][00980] Num frames 9400... +[2023-02-24 13:55:55,310][00980] Num frames 9500... +[2023-02-24 13:55:55,486][00980] Num frames 9600... +[2023-02-24 13:55:55,656][00980] Num frames 9700... +[2023-02-24 13:55:55,819][00980] Num frames 9800... +[2023-02-24 13:55:55,981][00980] Num frames 9900... +[2023-02-24 13:55:56,199][00980] Avg episode rewards: #0: 23.598, true rewards: #0: 9.998 +[2023-02-24 13:55:56,202][00980] Avg episode reward: 23.598, avg true_objective: 9.998 +[2023-02-24 13:55:56,206][00980] Num frames 10000... +[2023-02-24 13:56:56,166][00980] Replay video saved to /content/train_dir/default_experiment/replay.mp4! +[2023-02-24 13:57:22,832][00980] Loading existing experiment configuration from /content/train_dir/default_experiment/config.json +[2023-02-24 13:57:22,834][00980] Overriding arg 'num_workers' with value 1 passed from command line +[2023-02-24 13:57:22,837][00980] Adding new argument 'no_render'=True that is not in the saved config file! +[2023-02-24 13:57:22,839][00980] Adding new argument 'save_video'=True that is not in the saved config file! +[2023-02-24 13:57:22,841][00980] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file! +[2023-02-24 13:57:22,844][00980] Adding new argument 'video_name'=None that is not in the saved config file! +[2023-02-24 13:57:22,848][00980] Adding new argument 'max_num_frames'=100000 that is not in the saved config file! +[2023-02-24 13:57:22,850][00980] Adding new argument 'max_num_episodes'=10 that is not in the saved config file! +[2023-02-24 13:57:22,851][00980] Adding new argument 'push_to_hub'=True that is not in the saved config file! +[2023-02-24 13:57:22,852][00980] Adding new argument 'hf_repository'='mnavas/rl_course_vizdoom_health_gathering_supreme' that is not in the saved config file! +[2023-02-24 13:57:22,854][00980] Adding new argument 'policy_index'=0 that is not in the saved config file! +[2023-02-24 13:57:22,855][00980] Adding new argument 'eval_deterministic'=False that is not in the saved config file! +[2023-02-24 13:57:22,857][00980] Adding new argument 'train_script'=None that is not in the saved config file! +[2023-02-24 13:57:22,858][00980] Adding new argument 'enjoy_script'=None that is not in the saved config file! +[2023-02-24 13:57:22,860][00980] Using frameskip 1 and render_action_repeat=4 for evaluation +[2023-02-24 13:57:22,883][00980] RunningMeanStd input shape: (3, 72, 128) +[2023-02-24 13:57:22,885][00980] RunningMeanStd input shape: (1,) +[2023-02-24 13:57:22,898][00980] ConvEncoder: input_channels=3 +[2023-02-24 13:57:22,934][00980] Conv encoder output size: 512 +[2023-02-24 13:57:22,935][00980] Policy head output size: 512 +[2023-02-24 13:57:22,956][00980] Loading state from checkpoint /content/train_dir/default_experiment/checkpoint_p0/checkpoint_000000978_4005888.pth... +[2023-02-24 13:57:23,383][00980] Num frames 100... +[2023-02-24 13:57:23,490][00980] Num frames 200... +[2023-02-24 13:57:23,605][00980] Num frames 300... +[2023-02-24 13:57:23,714][00980] Num frames 400... +[2023-02-24 13:57:23,828][00980] Num frames 500... +[2023-02-24 13:57:23,940][00980] Num frames 600... +[2023-02-24 13:57:24,054][00980] Num frames 700... +[2023-02-24 13:57:24,167][00980] Num frames 800... +[2023-02-24 13:57:24,279][00980] Num frames 900... +[2023-02-24 13:57:24,389][00980] Num frames 1000... +[2023-02-24 13:57:24,472][00980] Avg episode rewards: #0: 22.240, true rewards: #0: 10.240 +[2023-02-24 13:57:24,475][00980] Avg episode reward: 22.240, avg true_objective: 10.240 +[2023-02-24 13:57:24,575][00980] Num frames 1100... +[2023-02-24 13:57:24,689][00980] Num frames 1200... +[2023-02-24 13:57:24,811][00980] Num frames 1300... +[2023-02-24 13:57:24,924][00980] Num frames 1400... +[2023-02-24 13:57:25,036][00980] Num frames 1500... +[2023-02-24 13:57:25,148][00980] Num frames 1600... +[2023-02-24 13:57:25,270][00980] Num frames 1700... +[2023-02-24 13:57:25,389][00980] Num frames 1800... +[2023-02-24 13:57:25,499][00980] Num frames 1900... +[2023-02-24 13:57:25,577][00980] Avg episode rewards: #0: 22.600, true rewards: #0: 9.600 +[2023-02-24 13:57:25,579][00980] Avg episode reward: 22.600, avg true_objective: 9.600 +[2023-02-24 13:57:25,669][00980] Num frames 2000... +[2023-02-24 13:57:25,783][00980] Num frames 2100... +[2023-02-24 13:57:25,905][00980] Num frames 2200... +[2023-02-24 13:57:26,018][00980] Num frames 2300... +[2023-02-24 13:57:26,130][00980] Num frames 2400... +[2023-02-24 13:57:26,242][00980] Num frames 2500... +[2023-02-24 13:57:26,361][00980] Num frames 2600... +[2023-02-24 13:57:26,477][00980] Num frames 2700... +[2023-02-24 13:57:26,607][00980] Num frames 2800... +[2023-02-24 13:57:26,693][00980] Avg episode rewards: #0: 21.094, true rewards: #0: 9.427 +[2023-02-24 13:57:26,696][00980] Avg episode reward: 21.094, avg true_objective: 9.427 +[2023-02-24 13:57:26,779][00980] Num frames 2900... +[2023-02-24 13:57:26,891][00980] Num frames 3000... +[2023-02-24 13:57:27,001][00980] Num frames 3100... +[2023-02-24 13:57:27,115][00980] Num frames 3200... +[2023-02-24 13:57:27,231][00980] Num frames 3300... +[2023-02-24 13:57:27,342][00980] Num frames 3400... +[2023-02-24 13:57:27,453][00980] Num frames 3500... +[2023-02-24 13:57:27,556][00980] Avg episode rewards: #0: 19.358, true rewards: #0: 8.857 +[2023-02-24 13:57:27,558][00980] Avg episode reward: 19.358, avg true_objective: 8.857 +[2023-02-24 13:57:27,635][00980] Num frames 3600... +[2023-02-24 13:57:27,748][00980] Num frames 3700... +[2023-02-24 13:57:27,870][00980] Num frames 3800... +[2023-02-24 13:57:27,981][00980] Num frames 3900... +[2023-02-24 13:57:28,109][00980] Num frames 4000... +[2023-02-24 13:57:28,204][00980] Avg episode rewards: #0: 16.846, true rewards: #0: 8.046 +[2023-02-24 13:57:28,206][00980] Avg episode reward: 16.846, avg true_objective: 8.046 +[2023-02-24 13:57:28,337][00980] Num frames 4100... +[2023-02-24 13:57:28,500][00980] Num frames 4200... +[2023-02-24 13:57:28,661][00980] Num frames 4300... +[2023-02-24 13:57:28,821][00980] Num frames 4400... +[2023-02-24 13:57:28,984][00980] Num frames 4500... +[2023-02-24 13:57:29,140][00980] Num frames 4600... +[2023-02-24 13:57:29,302][00980] Num frames 4700... +[2023-02-24 13:57:29,457][00980] Num frames 4800... +[2023-02-24 13:57:29,617][00980] Num frames 4900... +[2023-02-24 13:57:29,793][00980] Num frames 5000... +[2023-02-24 13:57:29,958][00980] Num frames 5100... +[2023-02-24 13:57:30,119][00980] Num frames 5200... +[2023-02-24 13:57:30,283][00980] Num frames 5300... +[2023-02-24 13:57:30,457][00980] Num frames 5400... +[2023-02-24 13:57:30,620][00980] Num frames 5500... +[2023-02-24 13:57:30,788][00980] Num frames 5600... +[2023-02-24 13:57:30,955][00980] Num frames 5700... +[2023-02-24 13:57:31,092][00980] Avg episode rewards: #0: 21.418, true rewards: #0: 9.585 +[2023-02-24 13:57:31,094][00980] Avg episode reward: 21.418, avg true_objective: 9.585 +[2023-02-24 13:57:31,179][00980] Num frames 5800... +[2023-02-24 13:57:31,343][00980] Num frames 5900... +[2023-02-24 13:57:31,515][00980] Num frames 6000... +[2023-02-24 13:57:31,682][00980] Num frames 6100... +[2023-02-24 13:57:31,813][00980] Num frames 6200... +[2023-02-24 13:57:31,932][00980] Num frames 6300... +[2023-02-24 13:57:32,043][00980] Num frames 6400... +[2023-02-24 13:57:32,153][00980] Num frames 6500... +[2023-02-24 13:57:32,264][00980] Num frames 6600... +[2023-02-24 13:57:32,382][00980] Num frames 6700... +[2023-02-24 13:57:32,495][00980] Num frames 6800... +[2023-02-24 13:57:32,613][00980] Num frames 6900... +[2023-02-24 13:57:32,725][00980] Avg episode rewards: #0: 22.359, true rewards: #0: 9.930 +[2023-02-24 13:57:32,726][00980] Avg episode reward: 22.359, avg true_objective: 9.930 +[2023-02-24 13:57:32,788][00980] Num frames 7000... +[2023-02-24 13:57:32,898][00980] Num frames 7100... +[2023-02-24 13:57:33,014][00980] Num frames 7200... +[2023-02-24 13:57:33,124][00980] Num frames 7300... +[2023-02-24 13:57:33,239][00980] Num frames 7400... +[2023-02-24 13:57:33,351][00980] Num frames 7500... +[2023-02-24 13:57:33,507][00980] Avg episode rewards: #0: 20.989, true rewards: #0: 9.489 +[2023-02-24 13:57:33,509][00980] Avg episode reward: 20.989, avg true_objective: 9.489 +[2023-02-24 13:57:33,522][00980] Num frames 7600... +[2023-02-24 13:57:33,636][00980] Num frames 7700... +[2023-02-24 13:57:33,769][00980] Num frames 7800... +[2023-02-24 13:57:33,889][00980] Num frames 7900... +[2023-02-24 13:57:33,999][00980] Num frames 8000... +[2023-02-24 13:57:34,109][00980] Num frames 8100... +[2023-02-24 13:57:34,218][00980] Num frames 8200... +[2023-02-24 13:57:34,330][00980] Num frames 8300... +[2023-02-24 13:57:34,446][00980] Num frames 8400... +[2023-02-24 13:57:34,563][00980] Num frames 8500... +[2023-02-24 13:57:34,678][00980] Num frames 8600... +[2023-02-24 13:57:34,796][00980] Num frames 8700... +[2023-02-24 13:57:34,909][00980] Num frames 8800... +[2023-02-24 13:57:35,045][00980] Avg episode rewards: #0: 22.523, true rewards: #0: 9.857 +[2023-02-24 13:57:35,047][00980] Avg episode reward: 22.523, avg true_objective: 9.857 +[2023-02-24 13:57:35,083][00980] Num frames 8900... +[2023-02-24 13:57:35,194][00980] Num frames 9000... +[2023-02-24 13:57:35,310][00980] Num frames 9100... +[2023-02-24 13:57:35,422][00980] Num frames 9200... +[2023-02-24 13:57:35,550][00980] Num frames 9300... +[2023-02-24 13:57:35,664][00980] Num frames 9400... +[2023-02-24 13:57:35,775][00980] Num frames 9500... +[2023-02-24 13:57:35,895][00980] Num frames 9600... +[2023-02-24 13:57:36,007][00980] Num frames 9700... +[2023-02-24 13:57:36,121][00980] Num frames 9800... +[2023-02-24 13:57:36,241][00980] Num frames 9900... +[2023-02-24 13:57:36,392][00980] Avg episode rewards: #0: 22.988, true rewards: #0: 9.988 +[2023-02-24 13:57:36,394][00980] Avg episode reward: 22.988, avg true_objective: 9.988 +[2023-02-24 13:58:35,822][00980] Replay video saved to /content/train_dir/default_experiment/replay.mp4!